Published Articles:
2020

"Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models", by Matthew Harding, Carlos Lamarche and M. Hashem Pesaran, Journal of Applied Econometrics, February 2020, Volume 35, Issue 3, pp. 294314.
Abstract: This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time‐of‐Use pricing using a large randomized control trial.
JEL Classifications: C21, C31, C33, D12, L94
Key Words: Common Correlated Effects; Dynamic Panel; Quantile Regression; Smart Meter; Randomized Experiment.
Full Text: http://dx.doi.org/10.1002/jae.2753
Appendix: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp20/qmg40rev21onlineappendix.pdf
2019

"Exponent of Crosssectional Dependence for Residuals", by Natalia Bailey, George Kapetanios and M. Hashem Pesaran, Sankhya B. The Indian Journal of Statistics, November 2019, pp. 157.
Abstract: In this paper we focus on estimating the degree of crosssectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of crosssectional dependence denoted by α, which is based on the number of nonzero pairwise cross correlations of these errors. We prove that our estimator, ã, is consistent and derive the rate at which ã, approaches its true value. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its FamaFrench extensions using 10year rolling samples from S&P 500 securities over the period Sept 1989  May 2018.
JEL Classifications: C21, C32
Key Words: Pairwise correlations, Crosssectional dependence, Crosssectional averages, Weak and strong factor models. CAPM and FamaFrench Factors.
Full Text: https://doi.org/10.1007/s13571019001969

"A Bayesian Analysis of Linear Regression Models with Highly Collinear Regressors", by M. Hashem Pesaran and Ron P. Smith, Econometrics and Statistics, July 2019, Volume 11, pp. 121.
Abstract: Exact collinearity between regressors makes their individual coefficients not identified. But, given an informative prior, their Bayesian posterior means are well defined. Just as exact collinearity causes nonidentification of the parameters, high collinearity can be viewed as weak identification of the parameters, which is represented, in line with the weak instrument literature, by the correlation matrix being of full rank for a finite sample size T, but converging to a rank deficient matrix as T goes to infinity. The asymptotic behaviour of the posterior mean and precision of the parameters of a linear regression model are examined in the cases of exactly and highly collinear regressors. In both cases the posterior mean remains sensitive to the choice of prior means even if the sample size is sufficiently large, and that the precision rises at a slower rate than the sample size. In the highly collinear case, the posterior means converge to normally distributed random variables whose mean and variance depend on the prior means and prior precisions. The distribution degenerates to fixed points for either exact collinearity or strong identification. The analysis also suggests a diagnostic statistic for the highly collinear case. Monte Carlo simulations and an empirical example are used to illustrate the main findings.
JEL Classifications: C11, C18
Key Words: Bayesian identification, multicollinear regressions, weakly identified regression coefficients, highly collinear regressors.
Full Text: https://doi.org/10.1016/j.ecosta.2018.10.001Note: A previous version of the paper was distributed as CESifo Working Paper 6785 under the title of "Posterior Means and Precisions of the Coefficients in Linear Models with Highly Collinear Regressors"

"Mean Group Estimation in Presence of Weakly CrossCorrelated Estimators", by Alexander Chudik and M. Hashem Pesaran, Economics Letters, February 2019, Volume 175, pp. 101105.
Abstract: This paper extends the mean group (MG) estimator for random coefficient panel data models by allowing the underlying individual estimators to be weakly cross correlated. This can arise, for example, in panels with spatially correlated errors. We establish that the MG estimator is asymptotically correctly centered, and its asymptotic covariance matrix can be consistently estimated. In contrast with the homogeneous case, the random coefficient speci cation allows for correct inference even when nothing is known about the weak crosssectional dependence of the errors.
JEL Classifications: C12, C13, C23.
Key Words: Mean Group Estimator, Cross Sectional Dependence, Spatial Models, Panel Data.
Full Text: https://doi.org/10.1016/j.econlet.2018.12.036

"A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices", by Natalia Bailey, M. Hashem Pesaran and L. Vanessa Smith, CAFE Research Paper No. 14.05, Journal of Econometrics, February 2019, Volume 208, Issue 2, pp. 507534.
Abstract: This paper proposes a regularisation method for the estimation of large covariance matrices that uses insights from the multiple testing (MT) literature. The approach tests the statistical signi cance of individual pairwise correlations and sets to zero those elements that are not statistically signi cant, taking account of the multiple testing nature of the problem. The effective pvalues of the tests are set as a decreasing function of N (the cross section dimension), the rate of which is governed by the nature of dependence of the underlying observations, and the relative expansion rates of N and T (the time dimension). In this respect, the method speci es the appropriate thresholding parameter to be used under Gaussian and nonGaussian settings. The MT estimator of the sample correlation matrix is shown to be consistent in the spectral and Frobenius norms, and in terms of support recovery, so long as the true covariance matrix is sparse. The performance of the proposed MT estimator is compared to a number of other estimators in the literature using Monte Carlo experiments. It is shown that the MT estimator performs well and tends to outperform the other estimators, particularly when N is larger than T.
JEL Classifications: C13, C58.
Key Words: Highdimensional data, Multiple testing, NonGaussian observations, Sparsity, Thresholding, Shrinkage.
Full Text: https://doi.org/10.1016/j.jeconom.2018.10.006
Supplementary Material: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp18/OnlinesupplementforBaileyPesaranandSmithRegularisationoflargecovariancematrixJoE2019.pdf
2018

"Doublequestion Survey Measures for the Analysis of Financial Bubbles and Crashes", by M. Hashem Pesarann and Ida Johnsson, Journal of Business and Economic Statistics, November 2018
Abstract: This paper proposes a new doublequestion survey whereby an individual is presented with two sets of questions; one on beliefs about current asset values and another on price expectations. A theoretical asset pricing model with heterogeneous agents is advanced and the existence of a negative relationship between price expectations and asset valuations is established, and is then tested using survey results on equity, gold and house prices. Leading indicators of bubbles and crashes are proposed and their potential value is illustrated in the context of a dynamic panel regression of realized house price changes across key Metropolitan Statistical Areas (MSAs) in the US. In an outofsample forecasting exercise it is also shown that forecasts of house price changes (pooled across MSAs) that make use of bubble and crash indicators perform signi ficantly better than a benchmark model that only uses lagged and expected house price changes.
JEL Classifications: C83, D84, G12, G14.
Key Words: Price expectations, bubbles and crashes, house prices, belief valuations.
Full Text: https://doi.org/10.1080/07350015.2018.1513845
Supplementary Materials: https://www.dropbox.com/s/qe2efuf4tge89gk/Supplementary%20Materials%20for%20Review.zip?dl=0
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp18/DQsurveydataAug2012Jan2013.zip
Read Me: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp18/readme.txt

"HalfPanel Jackknife Fixed Effects Estimation of Linear Panels with Weakly Exogenous Regressors", by Alexander Chudik, M. Hashem Pesarann and JuiChung Yang, SSRN Working Paper No. 281, Journal of Applied Econometrics, October 2018, Volume 33, Issue 6, pp. 816836.
Abstract: This paper considers estimation and inference in fixed effects (FE) linear panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when N (the cross section dimension) is large relative to T (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of FE estimator when N is large relative to T, the use of halfpanel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order [code], and for valid inference it is only required that N/T[code], as N, T [code] jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when N > T, with the proposed estimator showing little size distortions. The use of halfpanel jackknife FETE estimator is illustrated with two empirical applications from the literature.
JEL Classifications: C32, E17, E32, F44, F47, O51, Q43.
Key Words: Panel Data Models, Weakly Exogenous Regressors, Lagged Dependent Variable, Fixed Effects, Time Effects, Unbalanced Panels, HalfPanel Jackknife, Bias Correction
Full Text: https://doi.org/10.1002/jae.2623
Readme file: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp18/readme_file_for_data_and_codes.txt
Data for the Empirical Section: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp18/cpydata.zip
Computer codes: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp18/cpyprogs.zip

"Tests of Policy Interventions in DSGE Models", by M. Hashem Pesaran and Ron P. Smith, Oxford Bulletin of Economics and Statistics, June 2018, Volume 80, Issue 3, pp. 457484.
Abstract: This paper considers tests of the effectiveness of a policy intervention, defined as a change in the parameters of a policy rule, in the context of a macroeconometric dynamic stochastic general equilibrium (DSGE) model. We consider two types of intervention, first the standard case of a parameter change that does not alter the steady state, and second one that does alter the steady state, e.g. the target rate of inflation. We consider two types of test, one a multihorizon test, where the postintervention policy horizon, H, is small and fixed, and a mean policy effect test where H is allowed to increase without bounds. The multihorizon test requires Gaussian errors, but the mean policy effect test does not. It is shown that neither of these two tests are consistent, in the sense that the the power of the tests does not tend to unity as H → ∞, unless the intervention alters the steady state. This follows directly from the fact that DSGE variables are measured as deviations from the steady state, and the effects of policy change on target variables decay exponentially fast. We investigate the size and power of the proposed mean effect test by simulating a standard three equation New Keynesian DSGE model. The simulation results are in line with our theoretical findings and show that in all applications the tests have the correct size; but unless the intervention alters the steady state, their power does not go to unity with H.
JEL Classifications: C18, C54, E65.
Key Words: Counterfactuals, policy analysis, policy ineffectiveness test, macroeconomics.
Full Text: https://doi.org/10.1111/obes.12224
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp17/PS_on_PI_12_October_2017_online_supplement.pdf

"A OneCovariate at a Time, Multiple Testing Approach to Variable Selection in HighDimensional Linear Regression Models", by Alexander Chudik, George Kapetanios and M. Hashem Pesaran, Econometrica, July 2018, Volume 86, Issue 4, pp. 14791512.
Abstract: This paper provides an alternative approach to penalised regression for model selection in the context of high dimensional linear regressions where the number of covariates is large, often much larger than the number of available bbservations. We consider the statistical significance of individual covariates one at a time, whilst taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure, and use ideas from the multiple testing literature to control the probability of selecting the approximating model, the false positive rate and the false discovery rate. OCMT is easy to interpret, relates to classical statistical analysis, is valid under general assumptions, is faster to compute, and performs well in small samples. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation.
JEL Classifications: C52, C55
Key Words: One covariate at a time, multiple testing, model selection, high dimensionality, penalised regressions, boosting, Monte Carlo experiments.
Full Text: https://doi.org/10.3982/ECTA14176
MC Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp18/14176_OnlineMCsupplement.pdf
Empirical Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp18/14176_Onlineempiricalsupplement.pdf
Theory Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp18/14176_OnlineTheorySupplement.pdf
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp18/14176_Data_and_Programs_0.zip

"Estimation of Timeinvariant Effects in Static Panel Data Models", by M. Hashem Pesaran and Qiankun Zhou, Econometrics Reviews, May 2018, Volume 37, Issue 10. pp. 11371171.
Abstract: This paper proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEFIV) estimators for estimation and inference in the case of timeinvariant effects in static panel data models when N is large and T is fixed. The FEFIV allows for endogenous timeinvariant regressors but assumes that there exists a suficient number of instruments for such regressors. It is shown that the FEF and FEFIV estimators are [code]consistent, and asymptotically normally distributed. The FEF estimator is compared with the Fixed Effects Vector Decomposition (FEVD) estimator proposed by Plumper and Troeger (2007) and conditions under which the two estimators are equivalent are established. It is also shown that the variance estimator proposed for FEVD estimator is inconsistent and its use could lead to misleading inference. Alternative variance estimators are proposed for both FEF and FEFIV estimators which are shown to be consistent under fairly general conditions. The small sample properties of the FEF and FEFIV estimators are investigated by Monte Carlo experiments, and it is shown that FEF has smaller bias and RMSE, unless an intercept is included in the second stage of the FEVD procedure which renders the FEF and FEVD estimators identical. The FEVD procedure, however, results in substantial size distortions since it uses incorrect standard errors. In the case where some of the timeinvariant regressors are endogenous, the FEFIV procedure is compared with a modified version of Hausman and Taylor (1981) (HT) estimator. It is shown that both estimators perform well and have similar small sample properties. But the application of standard HT procedure, that incorrectly assumes a subset of timevarying regressors are uncorrelated with the individual effects, will yield biased estimates and significant size distortions.
JEL Classifications: C01, C23, C33.
Key Words: Static panel data models, timeinvariant effects, endogenous timeinvariant regressors, Monte Carlo experiments, fixed effects filtered estimators.
Full Text: https://doi.org/10.1080/07474938.2016.1222225
Supplementary Data: http://www.econ.cam.ac.uk/emeritus/mhp1/fp16/PesaranZhou_Timeinvariantestimation_May272016_supplement.pdf
Stata Code and Instructions: http://qiankunzhou.weebly.com/research.html

"To Pool or not to Pool: Revisited", by M. Hashem Pesaran and Qiankun Zhou, Oxford Bulletin of Economics and Statistics, April 2018, Volume 80, Issue 2, pp. 185217.
Abstract: This paper provides a new comparative analysis of pooled least squares and fixed effects estimators of the slope coefficients in the case of panel data models when the time dimension (T) is xed while the cross section dimension (N) is allowed to increase without bounds. The individual effects are allowed to be correlated with the regressors, and the comparison is carried out in terms of an exponent coefficients, δ, which measures the degree of pervasiveness of the fixed effects in the panel. The use of δ allows us to distinguish between poolability of small N dimensional panels with large T from large N dimensional panels with small T. It is shown that the pooled estimator remains consistent so long as δ < 1, and is asymptotically normally distributed if δ < 1/2, for a fixed T and as N → ∞. It is further shown that when δ < 1/2, the pooled estimator is more efficient than the fixed effects estimator. We also propose a Hausman type diagnostic test of δ < 1/2 as a simple test of poolability, and propose a pretest estimator that could be used in practice. Monte Carlo evidence supports the main theoretical findings and gives some indications of gains to be made from pooling when δ < 1/2.
JEL Classifications: C01, C23, C33
Key Words: Short panel, Fixed effects estimator, Pooled estimator, Efficiency.
Full Text: http://dx.doi.org/10.1111/obes.12220
2017

"Exponential class of dynamic binary choice panel data models with fixed effects", by Majid M. AlSadoon, Tong Li and M. Hashem Pesaran, CESifo Working Paper No. 4033, Econometrics Reviews, March 2017, Volume 36, Issue 69, pp. 898927.
Abstract: This paper proposes an exponential class of dynamic binary choice panel data models for the analysis of short T (time dimension) large N (cross section dimension) panel data sets that allows for unobserved heterogeneity (fixed effects) to be arbitrarily correlated with the covariates. The paper derives moment conditions that are in variant to the fixed effects which are then used to identify and estimate the parameters of the model. Accordingly, GMM estimators are proposed that are consistent and asymptotically normally distributed at the rootN rate. We also study the conditional likelihood approach, and show that under exponential specification it can identify the effect of state dependence but not the effects of other covariates. Monte Carlo experiments show satisfactory nite sample performance for the proposed estimators, and investigate their robustness to missspecification.
JEL Classifications: C23, C25
Key Words: Dynamic Discrete Choice, Fixed Effects, Panel Data, GMM, CMLE.
Full Text: http://www.tandfonline.com/doi/full/10.1080/07474938.2017.1307597
Supplement: http://www.econ.cam.ac.uk/emeritus/mhp1/pp17/SupplementtoAlSadoonLiandPesaranDBCAPE_probitMCexperimentsr.pdf

"Oil Prices and the Global Economy: Is It Different This Time Around?", by Kamiar Mohaddes and M. Hashem Pesaran, Energy Economics, June 2017, Volume 65, pp. 315325.
Abstract: The recent plunge in oil prices has brought into question the generally accepted view that lower oil prices are good for the US and the global economy. In this paper, using a quarterly multicountry econometric model, we first show that a fall in oil prices lowers interest rates and inflation in most countries, and increases global real equity prices. The effects on real output are positive, although they take longer to materialize (around 4 quarters after the shock). We then reexamine the effects of low oil prices on the US economy over different subperiods using monthly observations on real oil prices, real equity prices and real dividends. We confirm the perverse positive relationship between oil and equity prices over the period since the 2008 financial crisis highlighted in the recent literature, but show that this relationship has been unstable when considered over the longer time period of 1946–2016. In contrast, we find a stable negative relationship between oil prices and real dividends which we argue is a better proxy for economic activity (as compared to equity prices). On the supply side, the effects of lower oil prices differ widely across the different oil producers, and could be perverse initially, as some of the major oil producers try to compensate their loss of revenues by raising production. Taking demand and supply adjustments to oil price changes as a whole, we conclude that oil markets equilibrate but rather slowly, with large episodic swings between low and high oil prices.
Full Text: https://doi.org/10.1016/j.eneco.2017.05.011
Supplement: http://www.sciencedirect.com/science/MiamiMultiMediaURL/1s2.0S0140988317301548/1s2.0S0140988317301548mmc1.pdf/271683/html/S0140988317301548/38a33cfc137976ad26c777849c9a761f/mmc1.pdf
Data Package and the Matlab Files: http://www.sciencedirect.com/science/MiamiMultiMediaURL/1s2.0S0140988317301548/1s2.0S0140988317301548mmc2.zip/271683/html/S0140988317301548/49d5fc98e2fc39e38037894f5b9add1d/mmc2.zip

"Is There a Debtthreshold Effect on Output Growth?", by Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran, and Mehdi Raissi, forthcoming in the Review of Economics and Statistics, February 2017, Vol 99, pp. 135150.
Abstract: This paper studies the longrun impact of public debt expansion on economic growth and investigates whether the debtgrowth relation varies with the level of indebtedness. Our contribution is both theoretical and empirical. On the theoretical side, we develop tests for threshold effects in the context of dynamic heterogeneous panel data models with crosssectionally dependent errors and illustrate, by means of Monte Carlo experiments, that they perform well in small samples. On the empirical side, using data on a sample of 40 countries (grouped into advanced and developing) over the 19652010 period, we find no evidence for a universally applicable threshold effect in the relationship between public debt and economic growth, once we account for the impact of global factors and their spillover effects. Regardless of the threshold, however, we find significant negative longrun effects of public debt buildup on output growth. Provided that public debt is on a downward trajectory, a country with a high level of debt can grow just as fast as its peers.
JEL Classifications: C23, E62, F34, H6
Key Words: Panel tests of threshold effects, longrun relationships, estimation and inference, large dynamic heterogeneous panels, crosssection dependence, debt, and inflation.
Full Text: http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00593
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp16/Supplement_03July2015_3(uploadREStat).pdf
Matlab Codes for the CSDL Estimators: http://www.econ.cam.ac.uk/peoplefiles/faculty/km418/CMPR_CSDL.zip
Matlab Codes for Panel Tests of Threshold Effects: http://www.econ.cam.ac.uk/peoplefiles/faculty/km418/CMPR_Threshold_Codes.zip

"CountrySpecific Oil Supply Shocks and the Global Economy: A Counterfactual Analysis", by Kamiar Mohaddes and M. Hashem Pesaran, Energy Economics, September 2016, Volume 59, pp. 382399.
Abstract: This paper investigates the global macroeconomic consequences of countryspecific oilsupply shocks. Our contribution is both theoretical and empirical. On the theo retical side, we develop a model for the global oil market and integrate this within a compact quarterly model of the global economy to illustrate how our multicountry approach to modelling oil markets can be used to identify countryspecific oilsupply shocks. On the empirical side, estimating the GVAROil model for 27 countries/regions over the period 1979Q2 to 2013Q1, we show that the global economic implications of oilsupply shocks (due to, for instance, sanctions, wars, or natural disasters) vary considerably depending on which country is subject to the shock. In particular, we find that adverse shocks to Iranian oil output are neutralized in terms of their effects on the global economy (real outputs and financial markets) mainly due to an increase in Saudi Arabian oil production. In contrast, a negative shock to oil supply in Saudi Arabia leads to an immediate and permanent increase in oil prices, given that the loss in Saudi Arabian production is not compensated for by the other oil producers. As a result, a Saudi Arabian oil supply shock has significant adverse effects for the global economy with real GDP falling in both advanced and emerging economies, and large losses in real equity prices worldwide.
JEL Classifications: C32, E17, F44, F47, O53, Q43.
Key Words: Countryspecific oil supply shocks, identification of shocks, oil sanctions, oil prices, global oil markets, Iran, Saudi Arabia, international business cycle, Global VAR (GVAR), interconnectedness, impulse responses.
Full Text: http://dx.doi.org/10.1016/j.eneco.2016.08.007
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp16/MP_GVAR_Data.zip

"A Two Stage Approach to SpatioTemporal Analysis with Strong and Weak CrossSectional Dependence", by Natalia Bailey, Sean Holly, and M. HashemPesaran, CESifo Working Paper No. 4592, Journal of Applied Econometrics, 2016, Vol 31, Issue 1, pp. 249280.
Abstract: An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross unit averages to extract common factors (viewed as a source of strong crosssectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the defactored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatiotemporal model for the defactored real house price changes and obtain significant evidence of spatial connections, both positive and negative.
JEL Classifications: C21, C23
Key Words: Spatial and factor dependence, spatiotemporal models, positive and negative connections, house price changes.
Full Text: http://onlinelibrary.wiley.com/doi/10.1002/jae.2468/full

"LongRun Effects in Large Heterogenous Panel Data Models with CrossSectionally Correlated Errors", by Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran and Mehdi Raissi, Advances in Econometrics, 2016, Volume 36, pp. 85135, Essays in Honor of Aman Ullah.
Abstract: This paper develops a crosssectionally augmented distributed lag (CSDL) approach to the estimation of longrun effects in large dynamic heterogeneous panel data models with crosssectionally dependent errors. The asymptotic distribution of the CSDL estimator is derived under coefficient heterogeneity in the case where the time dimension (T) and the crosssection dimension (N) are both large. The CSDL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDLtype estimator, the CSDL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CSDL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.
JEL Classifications: C23.
Key Words: Longrun relationships, estimation and inference, panel distributed lags, large dynamic heterogeneous panels, crosssection dependence.
Full Text: http://dx.doi.org/10.1108/S0731905320160000036013

"Theory and Practice of GVAR Modeling", by Alexander Chudik, and M. Hashem Pesaran, SSRN Research Paper Series No. 14.04, Journal of Economic Surveys, February 2016, Volume 30, No. 1, pp. 165197.
Abstract: The Global Vector Autoregressive (GVAR) approach has proven to be a very useful approach to analyse interactions in the global macroeconomy and other data networks where both the crosssection and the time dimensions are large. This paper surveys the latest developments in the GVAR modelling, examining both the theoretical foundations of the approach and its numerous empirical applications. We provide a synthesis of existing literature and highlight areas for future research.
JEL Classifications: C32, E17.
Key Words: Global VAR, global macroeconometric modelling, global interdependencies, policy simulations.
Full Text: http://onlinelibrary.wiley.com/doi/10.1111/joes.12095/full

"A multicountry approach to forecasting output growth using PMIs", by Alexander Chudik, Valerie Grossmanz and M. Hashem Pesaran, Journal of Econometrics, January 2016, Vol 192, Issue 2, pp 349365.
Abstract: This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of a strong unobserved common factor can lead to an undetermined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability (as the panel dimensions N,T [code] ∞ such that N/T → k for some 0 < k < ∞) to the infeasible optimal forecasts obtained from a factoraugmented highdimensional VAR model. The small sample properties of the proposed solution are investigated by Monte Carlo experiments as well as empirically. In the empirical part, we investigate the value of the information content of Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from Aug GVAR models with a number of datarich forecasting methods, including Lasso, Ridge, partial least squares and factorbased methods. It is found that (a) regardless of the forecasting meth ods considered, PMIs are useful for nowcasting, but their value added diminishes quite rapidly with the forecast horizon, and (b) AugGVAR forecasts do as well as other datarich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.
JEL Classifications: C53, E37.
Key Words: Global VARs, Highdimensional VARs, Augmented GVAR, Forecasting, Nowcasting, Datarich methods, GDP and PMIs
Full Text: http://www.sciencedirect.com/science/article/pii/S0304407616300069

"Exponent of Crosssectional Dependence: Estimation and Inference", by Natalia Bailey, George Kapetanios and M. Hashem Pesaran, forthcoming in the Journal of Applied Econometrics, January 2016, Vol 31, pp. 9291196.
Abstract: In this paper, we provide a characterisation of the degree of crosssectional dependence in a two dimensional array, [code] in terms of the rate at which the variance of the crosssectional average of the observed data varies with N. We show that under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of [code] rises with N. We represent the degree of crosssectional dependence by , defined by the standard deviation, [code], where [code] is a simple crosssectional average of [code]. We refer to as the `exponent of crosssectional dependence', and show how it can be consistently estimated for values of > 1/2. We propose bias corrected estimators, derive their asymptotic properties and consider a number of extensions. We include a detailed Monte Carlo simulation study supporting the theoretical results. We also provide a number of empirical applications investigating the degree of interlinkages of real and financial variables in the global economy, the extent to which macroeconomic variables are interconnected across and within countries, and present recursive estimates of applied to excess returns on securities included in the Standard & Poor 500 index.
JEL Classifications: C21, C32
Key Words: Cross correlations, Crosssectional dependence, Crosssectional averages, Weak and strong factor models
Full Text: http://onlinelibrary.wiley.com/doi/10.1002/jae.v31.6/issuetoc
Supplementary Appendices: http://www.econ.cam.ac.uk/emeritus/mhp1/fp15/BKP_exponent_supplement_26_Jan_2015.pdf
Codes and Data: http://www.econ.cam.ac.uk/emeritus/mhp1/fp15/BKP_GAUSS_procedures.zip

"Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models with CrossSectional Heteroskedasticity", by Kazuhiko Hayakawa and M. Hashem Pesaran, CWPE Working Paper No. 1224, IZA Discussion Paper 6583, Cesifo Working Paper No.3850, Journal of Econometrics, Volume 188, Issue 1, September 2015, pp. 111134.
Abstract: This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are crosssectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem and its implications for estimation and inference. We approach the problem by working with a misspecified homoskedastic model, and then show that the transformed maximum likelihood estimator continues to be consistent even in the presence of crosssectional heteroskedasticity. We also obtain standard errors that are robust to crosssectional heteroskedasticity of unknown form. By means of Monte Carlo simulations, we investigate the finite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases.
JEL Classifications: C12, C13, C23
Key Words: Dynamic Panels, Crosssectional heteroskedasticity, Monte Carlo simulation, Transformed MLE, GMM estimation.
Full Text: http://www.sciencedirect.com/science/article/pii/S0304407615001244
Matlab Code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/wp12/MatlabcodeanddataforTransMLHayakawaandPesaran2012.zip
Supplementary Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp15/Supplement_04Nov2014.pdf

"Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors", by Alexander Chudik, and M. Hashem Pesaran,CESifo Working Paper No. 4232 and CAFE Research Paper No. 13.14, IZA Discussion Paper No. 6618, Journal of Econometrics, Volume 188, Issue 2, October 2015, pp. 393420.
Abstract: This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using covariates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive demeaning bias correction procedures to mitigate the small sample time series bias. Theoretical findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.
JEL Classifications: C31, C33
Key Words: Large panels, lagged dependent variable, cross sectional dependence, coefficient heterogeneity, estimation and inference, common correlated effects, unobserved common factors.
Full Text: http://www.sciencedirect.com/science/article/pii/S0304407615000767
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp14/Supplement_28Jan2014.pdf

"Testing Weak CrossSectional Dependence in Large Panels", by M. Hashem Pesaran, January 2012, CWPE Working Paper No. 1208, IZA Discussion Paper No. 6432, Econometric Reviews, Volume 34, Issue 610, May 2015, pp. 10891117.
Abstract: This paper considers testing the hypothesis that errors in a panel data model are weakly cross sectionally dependent, using the exponent of crosssectional dependence , introduced recently in Bailey, Kapetanios and Pesaran (2012). It is shown that the implicit null of the CD test depends on the relative expansion rates of N and T. When T = O , for some , then the implicit null of the CD test is given by , which gives , when N and T tend to infinity at the same rate such that T/N , with being a finite positive constant. It is argued that in the case of large N panels, the null of weak dependence is more appropriate than the null of independence which could be quite restrictive for large panels. Using Monte Carlo experiments, it is shown that the CD test has the correct size for values of in the range [0, 1/4], for all combinations of N and T, and irrespective of whether the panel contains lagged values of the dependent variables, so long as there are no major asymmetries in the error distribution.
JEL Classifications: C12, C13, C3
Key Words: Exponent of crosssectional dependence, Diagnostic tests, Panel data models, Dynamic heterogenous panels.
Full Text: http://www.tandfonline.com/doi/abs/10.1080/07474938.2014.956623

"Constructing MultiCountry Rational Expectations Models", by Stephane Dees, M. Hashem Pesaran, Ron P. Smith and L. Vanessa Smith, CESifo Working Papers No. 3081, Oxford Bulletin of Economics and Statistics, Volume 76, Issue 6, pp. 812840, December 2014.
Abstract: This paper considers some of the technical issues involved in using the GVAR approach to construct a multicountry rational expectations, RE, model and illustrates them with a new Keynesian model for 33 countries estimated with quarterly data over the period 19802011. The issues considered are: the measurement of steady states; the determination of exchange rates and the specification of the shortrun countryspecific models; the identification and estimation of the model subject to the theoretical constraints required for a determinate rational expectations solution; the solution of a large RE model; the structure and estimation of the covariance matrix; and the simulation of shocks. The model used as an illustration shows that global demand and supply shocks are the most important drivers of output, inflation and interest rates in the long run. By contrast, monetary or exchange rate shocks have only a shortrun impact in the evolution of the world economy. The paper also shows the importance of international connections, directly as well as indirectly through spillover effects. Overall, ignoring global interconnections as countryspecific models do, could give rise to misleading conclusions.
JEL Classifications: C32, E17, F37, F42
Key Words: Global VAR (GVAR), Multicountry New Keynesian (MCNK) models, supply shocks, demand shocks, monetary policy shocks
Full Text: http://onlinelibrary.wiley.com/doi/10.1111/obes.12046/abstract
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp13/DPPS_MCNK_Supplement_26June2013.pdf
Readme Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/wp10/ReadmeDataDPSS(2010).pdf
Transformed Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/wp10/TransformedData(1979Q12006Q4).zip
Source Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/wp10/SourceData(1979Q12006Q4).zip

"Large Panel Data Models with CrossSectional Dependence: A Survey", by Alexander Chudik, and M. Hashem Pesaran, CESifo WP Number 4371, in The Oxford Handbook on Panel Data edited by B. H. Baltagi, Oxford University Press, Ch. 1, ISBN: 9780199940042.
Abstract: This paper provides an overview of the recent literature on estimation and inference in large panel data models with crosssectional dependence. It reviews panel data models with strictly exogenous regressors as well as dynamic models with weakly exogenous regressors. The paper begins with a review of the concepts of weak and strong crosssectional dependence, and discusses the exponent of crosssectional dependence that characterizes the different degrees of crosssectional dependence. It considers a number of alternative estimators for static and dynamic panel data models, distinguishing between factor and spatial models of crosssectional dependence. The paper also provides an overview of tests of independence and weak crosssectional dependence.
JEL Classifications: C31, C33
Key Words: Large panels, weak and strong crosssectional dependence, factor structure, spatial dependence, tests of crosssectional dependence.
Book Link: http://ukcatalogue.oup.com/product/9780199940042.do

"Signs of Impact Effects in Time Series Regression Models", by M. Hashem Pesaran and Ron P Smith, (2014), Economics Letters, Vol. 122, Issue 2, pp.150153.
Abstract: In this paper we consider the problem of interpreting the signs of the estimated coeficients in multivariate time series regressions where the regressors are correlated. Using a continuous time model, we argue that focussing on the signs of individual coeficients in such regressions could be misleading and argue in favour of allowing for the indirect effects that arise due to the historical correlations amongst the regressors. For estimation from discrete time data we show that the sign of the total impact, including the direct and indirect effects, of a regressor can be obtained using a simple regression that only includes the regressor of interest.
JEL Classifications: C1, C5
Key Words: Regression coeficients, Impact effects.
Full Text: http://www.sciencedirect.com/science/article/pii/S0165176513005028

"One Hundred Years of Oil Income and the Iranian Economy: A Curse or a Blessing?", by Kamiar Mohaddes, and M. Hashem Pesaran, (2014), Iran and the Global Economy: Petro Populism, Islam and Economic Sanctions edited by Parvin Alizadeh and Hassan Hakimian, Routledge, Ch. 1, pp.1245. ISBN10: 0415505542 ISBN13: 9780415505543.
Abstract: This paper examines the impact of oil revenues on the Iranian economy over the past hundred years, spanning the period 1908–2010. It is shown that although oil has been produced in Iran over a very long period, its importance in the Iranian economy was relatively small up until the early 1960s. It is argued that oil income has been both a blessing and a curse. Oil revenues when managed appropriately are a blessing, but their volatility (which in Iran is much higher than oil price volatility) can have adverse effects on real output, through excessively high and persistent levels of inflation. Lack of appropriate institutions and policy mechanisms which act as shock absorbers in the face of high levels of oil revenue volatility have also become a drag on real output. In order to promote growth, policies should be devised to control inflation; to serve as shock absorbers negating the adverse effects of oil revenue volatility; to reduce rent seeking activities; and to prevent excessive dependence of government finances on oil income.
JEL Classifications: E02, N15, Q32
Key Words: Oil price volatility, oil income, rent seeking, inflation, macroeconomic policy.
Full Text: http://www.taylorandfrancis.com/books/details/9780415505543/

"An Empirical Growth Model for Major Oil Exporters", by Hadi Salehi Esfahani, Kamiar Mohaddes and M. Hashem Pesaran, (2014), Journal of Applied Econometrics, Vol. 29, Issue 1, pp. 121.
Abstract: This paper develops a longrun output relation for a major oil exporting economy where the oil income to output ratio remains sufficiently high over a prolonged period. It extends the stochastic growth model developed in Binder and Pesaran (1999) by including oil exports as an additional factor in the capital accumulation process. The paper distinguishes between the two cases where the growth of oil income, g0, is less than the natural growth rate (the sum of the population growth, n, and the growth of technical progress, g), and when g0 > g+n. Under the former, the effects of oil income on the economy's steady growth rate will vanish eventually, whilst under the latter, oil income enters the longrun output equation with a coefficient which is equal to the share of capital if it is further assumed that the underlying production technology can be represented by a CobbDouglas production function. The longrun theory is tested using quarterly data on nine major oil economies. Overall, the test results support the longrun theory, with the existence of longrun relations between real output, foreign output and real oil income established for six of the nine economies considered.
JEL Classifications: C32, C53, E17, F43, F47, Q32
Key Words: Growth models, longrun and errorcorrecting relations, major oil exporters, OPEC member countries, oil exports and foreign output shocks.
Full Text: http://onlinelibrary.wiley.com/doi/10.1002/jae.2294/abstract
Data: http://www.econ.cam.ac.uk/peoplefiles/faculty/km418/EMP_Data.zip

"Aggregation in Large Dynamic Panels", by M. Hashem Pesaran, Alexander Chudik, (2014), Journal of Econometrics, Vol 178, Issue 2, pp. 273285.
Abstract: This paper investigates the problem of aggregation in the case of large linear dynamic panels, where each micro unit is potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following Pesaran (2003), an optimal aggregate function is derived and used (i) to establish conditions under which Granger's (1980) conjecture regarding the long memory properties of aggregate variables from 'a very large scale dynamic, econometric model' holds, and (ii) to show which distributional features of micro parameters can be identified from the aggregate model. The paper also derives impulse response functions for the aggregate variables, distinguishing between the effects of macro and aggregated idiosyncratic shocks. Some of the findings of the paper are illustrated by Monte Carlo experiments. The paper also contains an empirical application to consumer price inflation in Germany, France and Italy, and reexamines the extent to which 'observed' inflation persistence at the aggregate level is due to aggregation and/or common unobserved factors. Our findings suggest that dynamic heterogeneity as well as persistent common factors are needed for explaining the observed persistence of the aggregate inflation.
JEL Classifications: C43, E31
Key Words: Aggregation, Large Dynamic Panels, Long Memory, Weak and Strong Cross Section Dependence, VAR Models, Impulse Responses, Factor Models, Inflation Persistence.
Full Text: http://www.sciencedirect.com/science/article/pii/S0304407613001917<

"Optimal Forecasts in the Presence of Structural Breaks", by M. Hashem Pesaran, Andreas Pick and Mikhail Pranovich, (2013), Journal of Econometrics, Vol. 177, Issue 2, pp 134152, ISSN 03044076
Abstract: This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for one step ahead forecasts. Under continuous breaks, our approach largely recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for optimal weights in models with a single regressor, and asymptotically valid weights for models with more than one regressor. It is shown that in these cases the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain, a forecasting procedure based on robust optimal weights is proposed. The relative performance of our proposed approach is investigated using Monte Carlo experiments and an empirical application to forecasting real GDP using the yield curve across nine industrial economies.
JEL Classifications: C22, C53
Key Words: Forecasting, Structural breaks, Optimal weights, Robust optimal weights,Exponential smoothing.
Full Text: http://www.sciencedirect.com/science/article/pii/S0304407613000687
Suppliment: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp13/PPWebSupplement.pdf

"On identification of Bayesian DSGE Models", by Gary Koop, M. Hashem Pesaran and Ron P. Smith, (2013), Journal of Business Economics and Statistics, Vol. 31, Issue 3, pp. 300314.
Abstract: This article is concerned with local identification of individual parameters of dynamic stochastic general equilibrium (DSGE) models estimated by Bayesian methods. Identification is often judged by a comparison of the posterior distribution of a parameter with its prior. However, these can differ even when the parameter is not identified. Instead, we propose two Bayesian indicators of identification. The first follows a suggestion by Poirier of comparing the posterior density of the parameter of interest with the posterior expectation of its prior conditional on the remaining parameters. The second examines the rate at which the posterior precision of the parameter gets updated with the sample size, using data simulated at the parameter point of interest for an increasing sequence of sample sizes (T). For identified parameters, the posterior precision increases at rate T. For parameters that are either unidentified or are weakly identified, the posterior precision may get updated but its rate of update will be slower than T. We use empirical examples to demonstrate that these methods are useful in practice. This article has online supplementary material.
JEL Classifications: C11, C15, E17
Key Words: Bayesian identification, weak identification, DSGE models, posterior updating.
Full Text: http://amstat.tandfonline.com/doi/full/10.1080/07350015.2013.773905#.UntwAPm1sI

"Oil Exports and the Iranian Economy", by Hadi Salehi Esfahani, Kamiar Mohaddes and M. Hashem Pesaran, (2013), The Quarterly Review of Economics and Finance, Vol. 53, Issue 3, pp. 221237.
Abstract: This paper presents an errorcorrecting macroeconometric model for the Iranian economy estimated using a new quarterly data set over the period 1979Q12006Q4. It builds on a recent paper by the authors, Esfahani et al. (2012), which develops a theoretical longrun growth model for major oil exporting economies. The core variables included in this paper are real output, real money balances, inflation, exchange rate, oil exports, and foreign real output, although the role of investment and consumption are also analyzed in a submodel. The paper finds clear evidence for the existence of two longrun relations: an output equation as predicted by the theory and a standard real money demand equation with inflation acting as a proxy for the (missing) market interest rate. The results show that real output in the long run is influenced by oil exports and foreign output. However, it is also found that inflation has a significant negative longrun effect on real GDP, which is suggestive of economic inefficiencies and is matched by a negative association between inflation and the investmentoutput ratio. Finally, the results of impulse responses show that the Iranian economy adjusts quite quickly to the shocks in foreign output and oil exports, which could be partly due to the relatively underdeveloped nature of Iran's financial markets.
JEL Classifications: C32, C53, E17, F43, F47, Q32
Key Words: Growth models, longrun relations, oil exporters, Iranian economy, oil price and foreign output shocks, and errorcorrecting relations.
Full Text: http://www.sciencedirect.com/science/article/pii/S1062976912000464

"Panel Unit Root Test in the Presence of a Multifactor Error Structure", M. Hashem Pesaran, L. V. Smith, and T. Yamagata, (2013), in Journal of Econometrics, vol 175, no 2, pp. 94115.
Abstract: This paper extends the crosssectionally augmented panel unit root test (CIPS) proposed by Pesaran (2007) to the case of a multifactor error structure, and proposes a new panel unit root test based on a simple average of crosssectionally augmented SarganBhargava statistics (CSB). The basic idea is to exploit information regarding the m unobserved factors that are shared by k observed time series in addition to the series under consideration. Initially, we develop the tests assuming that m0, the true number of factors is known, and show that the limit distribution of the tests does not depend on any nuisance parameters, so long asSmall sample properties of the tests are investigated by Monte Carlo experiments and are shown to be satisfactory. Particularly, the proposed CIPS and CSB tests have the correct size for all combinations of the cross section (N) and time series (T) dimensions considered. The power of both tests rise with N and T, although the CSB test performs better than the CIPS test for smaller sample sizes. The various testing procedures are illustrated with empirical applications to real interest rates and real equity prices across countries.
Key Words: Panel unit root tests, Cross section dependence, Multifactor error structure, Fisher inflation parity, Real equity prices.
JEL Classifications: C12, C15, C22, C23
Full Text: http://authors.elsevier.com/sd/article/S0304407613000353
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp13/20121022PSY_Supplement(MSNo2009229).pdf
Gauss Codes: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp13/Gauss_Code.zip
Elsevier Highly Cited Research Certificate: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp13/ElsevierHighlyCitedAwardCert010417.pdf

"Econometric Analysis of High Dimensional VARs Featuring a Dominant Unit", by M. Hashem Pesaran and Alexander Chudik, (2013), Econometrics Review, vol 32, pp. 592649.
Abstract: This paper extends the analysis of infinite dimensional vector autoregressive models (IVAR) proposed in Chudik and Pesaran (2010) to the case where one of the variables or the cross section units in the IVAR model is dominant or pervasive. This extension is not straightforward and involves several technical difficulties. The dominant unit influences the rest of the variables in the IVAR model both directly and indirectly, and its effects do not vanish even as the dimension of the model (N) tends to infinity. The dominant unit acts as a dynamic factor in the regressions of the nondominant units and yields an infinite order distributed lag relationship between the two types of units. Despite this it is shown that the effects of the dominant unit as well as those of the neighborhood units can be consistently estimated by running augmented least squares regressions that include distributed lag functions of the dominant unit. The asymptotic distribution of the estimators is derived and their small sample properties investigated by means of Monte Carlo experiments.
JEL Classifications: C10, C33, C51
Key Words: Dominant Units, Factor Models, IVAR Models, Large Panels, Star Networks, Spatial Models, Weak and Strong Cross Section Dependence
Full Text: http://www.tandfonline.com/doi/abs/10.1080/07474938.2012.740374#.Uaorn0Csh8F

"China's Emergence in the World Economy and Business Cycles in Latin America", by Ambrogio CesaBianchi, M. Hashem Pesaran, Alessandro Rebucci and TengTeng Xu, (2012), CWPE, IZA Discussion Paper, Economia, Journal of the Latin American and Caribbean Economic Association, vol 12, pp. 175.
Abstract: This paper investigates how changes in trade linkages between China, Latin America, and the rest of the world have altered the transmission of international business cycles to Latin America. Evidence based on a GVAR model for five large Latin American economies shows that the longterm impact of a China GDP shock on the typical Latin American economy has increased by three times since the mid1990s, while the longterm
impact of a US GDP shock has halved, while the transmission of shocks to Latin America and the rest of emerging Asia GDP (excluding China and India) has not changed. These changes owe more changes in China’s impact on Latin America’s traditional and largest trading partners than to increased direct bilateral trade linkages boosted by the decadelong commodity price boom. These findings have important implications for both Latin America and the international business cycle.
JEL Classifications: C32, F44, E32, O54
Key Words: China, GVAR, Great Recession, Emerging Markets, International Business Cycle, Latin America, Trade linkages.
Full Text: http://www.cid.harvard.edu/Economia/contents/contents_Spring%202012.html

"Diagnostic Tests of Cross Section Independence for Limited Dependent Variable Panel Data Models", by Cheng Hsiao, M. Hashem Pesaran and Andreas Pick, (2012), Oxford Bulletin of Economics and Statistics, vol 74, no 2, pp. 253277.
Abstract: This paper considers the problem of testing for cross section independence in limited dependent variable panel data models. It derives a Lagrangian multiplier (LM) test and shows that in terms of generalized residuals of Gourieroux, Monfort, Renault and Trognon (1987) it reduces to the LM test of Breusch and Pagan (1980). Due to the tendency of the LM test to overreject in panels with large N (cross section dimension), we also consider the application of the cross section dependence test (CD) proposed by Pesaran (2004). In Monte Carlo experiments it emerges that for most combinations of N and T the CD test is correctly sized, whereas the validity of the LM test requires T (time series dimension) to be quite large relative to N. We illustrate the crosssectional independence tests by an application to a probit panel of rollcall votes in the U. S. Congress and find that the votes display a significant degree of cross section dependence.
JEL Classifications: C12, C33, C35
Key Words: Nonlinear panels, cross section dependence, probit and Tobit models
Full Text: http://onlinelibrary.wiley.com/doi/10.1111/j.14680084.2011.00646.x/abstract

"On the Interpretation of Panel Unit Root Tests", by M. Hashem Pesaran, (2012), Economics Letters, vol 116, pp. 545546.
Abstract: Applications of panel unit root tests have become commonplace in empirical economics, yet there are ambiguities as how best to interpret the test results. This note clarifies that rejection of the panel unit root hypothesis should be interpreted as evidence that a statistically significant proportion of the units are stationary. Accordingly, in the event of a rejection, and in applications where the time dimension of the panel is relatively large, it recommends the test outcome to be augmented with an estimate of the proportion of the crosssection units for which the individual unit root tests are rejected. The economic importance of the rejection can be measured by the magnitude of this proportion.
JEL Classifications: C12, C33, C52
Key Words: Unit Root tests, Panels, Statistical Significance.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp12/PesaranEconLettes2012interpretingpanelunitroottests.pdf

"Comment on `Fast Sparse Regression and Classication' by J. H.Friedman", First author: George Kapetanios and Corresponding author: M. Hashem Pesaran, (2012), International Journal of Forecasting, vol 28, issue 3, pp. 739740.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp12/CommentonFriedmanIJF2012Published.pdf

"Beyond the DSGE Straitjacket, by M. Hashem Pesaran and Ron P. Smith, (2011), The Manchester School, vol 79, pp. 5–16.
Abstract: Academic macroeconomics and the research department of central banks have come to be dominated by Dynamic, Stochastic, General Equilibrium (DSGE) models based on microfoundations of optimising representative agents with rational expectations. We argue that the dominance of this particular sort of DSGE and the resistance of some in the profession to alternatives has become a straitjacket that restricts empirical and theoretical experimentation and inhibits innovation and that the profession should embrace a more flexible approach to macroeconometric modelling. We describe one possible approach.
JEL Classifications: C100, E100
Key Words: macroeconometric models, DSGE, VARs, long run theory.
Full Text: http://onlinelibrary.wiley.com/doi/10.1111/j.14679957.2011.02265_2.x/abstract 
"Forecast Combination across Estimation Windows", by M. Hashem Pesaran and Andreas Pick (2011), Journal of Business Economics and Statistics, vol 29, pp. 307318.

Abstract: This paper considers combining forecasts generated from the same model but over different estimation windows. It develops theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error than forecasts based on a single estimation window for all but the smallest breaks. An application to weekly returns on 20 equity index futures shows that averaging forecasts over estimation windows leads to a smaller RMSFE than some competing methods.
Key Words: Forecast averaging, estimation windows, exponential downweighting, structural breaks.
JEL Classifications: C22, C53.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp10/AveW4Mar2010.pdf

"Lumpy Price Adjustments: A Microeconometric Analysis", Emmanuel Dhyney, Catherine Fuss, M. Hashem Pesaran, Patrick Sevestre, (2011), Journal of Business Economics and Statistics, vol 29, no 4, pp. 529540
Abstract: Based on a reduced form statedependent pricing model with random thresholds, we specify and estimate a nonlinear panel data model with an unobserved factor representing the common cost or demand components of the unobserved optimal price. Using this model we are able to assess the relative importance of common and idiosyncratic shocks in explaining the frequency and magnitude of price changes in the case of a large variety of consumer products in Belgium and France. We find that the mean level and variability of the random thresholds are key for explaining differences across products in the frequency of price changes. We also find that the idiosyncratic shocks are the most important driver of the magnitude of price changes.
JEL Classifications: C51, C81, D21
Key Words: Sticky prices, idiosyncratic shocks, micro nonlinear panels
Full Text: http://pubs.amstat.org/doi/abs/10.1198/jbes.2011.09066
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp11/Final_version_DFPS_Supplements.pdf

"Panels With Nonstationary Multifactor Error Structures", by G. Kapetanios, M. Hashem Pesaran and T. Yamagata (2011), Journal of Econometrics, vol 160, issue 2, pp. 326348
Abstract: The presence of crosssectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of crosssectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes. The extension to I(1) processes is remarkable on two counts. First, it is of great interest to note that while intermediate results needed for deriving the asymptotic distribution of the panel estimators differ between the I(1) and I(0) cases, the final results are surprisingly similar. This is in direct contrast to the standard distributional results for I(1) processes that radically differ from those for I(0) processes. Second, it is worth noting the significant extra technical demands required to prove the new results. The theoretical findings are further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the crosssectionalaveragebased method is robust to a wide variety of data generation processes and has lower biases than the alternative estimation methods considered in the paper.
JEL Classifications: C12, C13, C33.
Key Words: Cross Section Dependence, Large Panels, Unit Roots, Principal Components, Common Correlated Effects.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp11/KapetaniosPesaranandYamagataJoE2011CCEUnit.pdf

"Weak and Strong Cross Section Dependence and Estimation of Large Panels", by Alexander Chudik, M. Hashem Pesaran and Elisa Tosetti (2011), The Econometrics Journal, vol 14, pp. C45C90
Abstract: This paper introduces the concepts of timespecific weak and strong cross section dependence, and investigates how these notions are related to the concepts of weak, strong and semistrong common factors, frequently used for modelling residual cross section correlations in panel data models. It then focuses on the problems of estimating slope coefficients in large panels, where cross section units are subject to possibly a large number of unobserved common factors. It is established that the Common Correlated Effects (CCE) estimator introduced by Pesaran (2006) remains asymptotically normal under certain conditions on factors loadings of an infinite factor error structure, including cases where methods relying on principal components fail. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.
JEL Classifications: Common correlated effects (CCE) estimator, Panels, Strong and weak crosssection dependence, Weak and strong factors.
Key Words: C10, C31, C33.
Full Text: http://onlinelibrary.wiley.com/doi/10.1111/j.1368423X.2010.00330.x/abstract

"Large Panels with Common Factors and Spatial Correlation", M. Hashem Pesaran and Elisa Tosseti (2011), Journal of Econometrics, vol 161, pp. 182202
Abstract: This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross section dependence. It introduces a general framework where error cross section dependence may arise because of unobserved common effects and/or error spillover effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the mean group and pooled estimators under heterogeneous and homogeneous slope coefficients, and for these estimators proposes nonparametric variance matrix estimators. The paper then considers the more general case of a panel data model with a multifactor error structure and spatial error correlations. Under this framework, the Common Correlated Effects (CCE) estimator, recently advanced by Pesaran (2006), continues to yield estimates of the slope coefficients that are consistent and asymptotically normal. Small sample properties of the estimators under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors.
JEL Classifications: C10, C31, C33
Key Words: Panels, Common Factors, Spatial Dependence, Common Correlated Effects Estimator.
Full Text: http://dx.doi.org/10.1016/j.jeconom.2010.12.003

"Variable Selection, Estimation and Inference for Multiperiod Forecasting Problems", by M. Hashem Pesaran, A. Pick and A. Timmerman (2011), Journal of Econometrics, vol 164, issue 1, pp. 173187
Abstract: This paper conducts a broadbased comparison of iterated and direct multiperiod forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factoraugmented vector autoregressions. To account for serial correlation in the residuals of the multiperiod direct forecasting models we propose a new SUREbased estimation method and modified Akaike information criteria for model selection. Empirical analysis of the 170 variables studied by Marcellino, Stock and Watson (2006) shows that information in factors helps improve forecasting performance for most types of economic variables although it can also lead to larger biases. It also shows that SURE estimation and finitesample modifications to the Akaike information criterion can improve the performance of the direct multiperiod forecasts.
JEL Classifications: C22, C32, C52, C53
Key Words: Direct forecasts, iterated forecasts, factor augmented VARs, SURE estimation, Akaike information criterion.
Full Text: http://dx.doi.org/10.1016/j.jeconom.2011.02.018

"Infinite Dimensional VARs and Factor Models", Alexander Chudik and M. Hashem Pesaran (2011), Journal of Econometrics, vol 163, pp. 422
Abstract: This paper proposes a novel approach for dealing with the ‘curse of dimensionality’ in the case of infinite dimensional vector autoregressive (IVAR) models. It is assumed that each unit or variable in the IVAR is related to a small number of neighbors and a large number of nonneighbors. The neighborhood effects are fixed and do not change with the number of units (N), but the coefficients of nonneighboring units are restricted to vanish in the limit as N tends to infinity. Problems of estimation and inference in a stationary IVAR model with an unknown number of unobserved common factors are investigated. A cross section augmented least squares (CALS) estimator is proposed and its asymptotic distribution is derived. Satisfactory small sample properties are documented by Monte Carlo experiments. An empirical illustration shows the statistical significance of dynamic spillover effects in modelling of U.S. real house prices across the neighboring States.
JEL Classifications: C10, C33, C51
Key Words: Large N and T Panels, Weak and Strong Cross Section Dependence, VARs, Spatial Models, Factor Models.
Full Text: http://dx.doi.org/10.1016/j.jeconom.2010.11.002

"Predictability of Asset Returns and the Efficient Market Hypothesis", by M. Hashem Pesaran (2011), Handbook of Empirical Economics and Finance, edited by Aman Ullah and David E. Giles, Taylor & Francis, ISBN10: 1420070355, ISBN13: 9781420070354.
Abstract: This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk aversion and market efficiency. The paper then focuses on the theoretical foundation of the EMH, and show that market efficiency could coexit with heterogeneous beliefs and individual irrationality so long as individual errors are cross sectionally weakly dependent in the sense defined by Chudik, Pesaran, and Tosetti (2010). But at times of market euphoria or gloom these individual errors are likely to become cross sectionally strongly dependent and the collective outcome could display significant departures from market efficiency. Market efficiency could be the norm, but it is likely to be punctuated with episodes of bubbles and crashes. The paper also considers if market inefficiencies (assuming that they exist) can be exploited for profit.
JEL Classifications: G12, G14
Key Words: Market Efficiency, Predictability, Heterogeneity of Expectations, Forecast averaging, Equity Premium Puzzle.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp10/AssetReturnsMEH31May2010.pdf
Link to Book: http://www.amazon.co.uk/HandbookEmpiricalEconomicsFinanceStatistics/dp/1420070355/ref=sr_1_1?s=books&ie=UTF8&qid=1334565573&sr=11

"The Spatial and Temporal Diffusion of House Prices in the UK", by Sean Holly, M. Hashem Pesaran and Takashi Yamagata (2011), Journal of Urban Economics, vol 69, pp. 223

Abstract: This paper provides a method for the analysis of the spatial and temporal diffusion of shocks in a dynamic system. We use changes in real house prices within the UK economy at the level of regions to illustrate its use. Adjustment to shocks involves both a region specific and a spatial effect. Shocks to a dominant region  London  are propagated contemporaneously and spatially to other regions. They in turn impact on other regions with a delay. We allow for lagged effects to echo back to the dominant region. London in turn is influenced by international developments through its link to New York and other financial centers. It is shown that New York house prices have a direct effect on London house prices. We analyse the effect of shocks using generalised spatiotemporal impulse responses. These highlight the diffusion of shocks both over time (as with the conventional impulse responses) and over space.
Key Words: House Prices, Cross Sectional Dependence, Spatial Dependence.
JEL Classifications: C21, C23
Full Text: http://dx.doi.org/10.1016/j.jue.2010.08.002
Data and Supplemental Results: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp10/UKHP_DATA_Supplement.zip
Gauss codes and Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp11/UKHP_for_UK_Results_Gauss6.zip

"Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash", Bahram Pesaran and M. Hashem Pesaran (2010), Special Issue of Economic Modelling in honor of PAVB Swamy, edited by Stephen G. Hall, Lawrence R. Klein, George S. Tavlas and Arnold Zellner, vol 27, pp. 13981416
Abstract: Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the tDCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.
JEL Classifications: C51, C52, G11
Key Words: Volatilities and Correlations, Weekly Returns, Multivariate t, Financial Interdependence, VaR diagnostics, 2008 Stock Market Crash.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp10/PesaranandPesaranEconomicModelling2010.pdf

"A SpatioTemporal Model Of House Prices In The US", by Sean Holly, M. Hashem Pesaran and Takashi Yamagata (2010), Journal of Econometrics, vol 158, pp. 160173
Abstract: This paper provides an empirical analysis of changes in real house prices in the USA using State level data. It examines the extent to which real house prices at the State level are driven by fundamentals such as real per capita disposable income, as well as by common shocks, and determines the speed of adjustment of real house prices to macroeconomic and local disturbances. We take explicit account of both crosssectional dependence and heterogeneity.This allows us to find a cointegrating relationship between real house prices and real per capita incomes with coefficients (1,  1), as predicted by the theory. We are also able to identify a significant negative effect for a net borrowing cost variable, and a significant positive effect for the State level population growth on changes in real house prices. Using this model we then examine the role of spatial factors, in particular, the effect of contiguous states by use of a weighting matrix. We are able to identify a significant spatial effect, even after controlling for State specific real incomes, and allowing for a number of unobserved common factors. We do, however, find evidence of departures from long run equilibrium in the housing markets in a number of States notably California, NewYork, Massachusetts, and to a lesser extent Connecticut, Rhode Island, Oregon and Washington State.
JEL Classifications: C21, C23.
Key Words: House Price, Cointegration, Cross Sectional Dependence, Spatial Dependence.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp10/ECONOM3363.pdf
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/HPY_HousePriceData19Aug09.zip
Gauss Code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp10/Code_for_Table9_10_HollyPesaranYamagata2010.zipReprinted as Chapter 9 in Volume II of Yannis M. Ioannides (Ed.), Recent Developments in the Economics of Housing
The International Library of Critical Writings in Economics series, 2019, Edward Elgar Publishing.

"A VECX* Model of the Swiss Economy", by Katrin AssenmacherWesche and M. Hashem Pesaran (2009) Swiss National Bank Economic Studies, no 6.

Abstract: This paper applies the modelling strategy of Garratt, Lee, Pesaran and Shin (2003) to the estimation of a structural cointegrated VAR model that relates the core macroeconomic variables of the Swiss economy to current and lagged values of a number of key foreign variables. We identify and test a longrun structure between the variables. Moreover, we analyse the dynamic properties of the model using Generalised Impulse Response Functions. In its current form the model can be used to produce forecasts for the endogenous variables either under alternative specifications of the marginal model for the exogenous variables, or conditional on some prespecified path of those variables (for scenario forecasting). In due course the Swiss VECX* model can also be integrated within a Global VAR (GVAR) model where the foreign variables of the model are determined endogenously.
Key Words: Longrun structural vector autoregression.
JEL Classifications: C53, C32.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp09/Economic_Study_20096.pdf

"Identification of New Keynesian Phillips Curves from a Global Perspective", by Stephane Dees, M. Hashem Pesaran, L. Vanessa Smith and Ron P. Smith (2009), Journal of Money, Credit and Banking, vol 41, issue 7, pp. 14811502

Abstract: This paper is concerned with the estimation of New Keynesian Phillips Curves (NKPC) and focusses on two issues: the weak instrument problem and the characterisation of the steady states. It proposes some solutions from a global perspective. Using a global vector autoregressive model (GVAR) steady states are estimated as longhorizon expectations and valid instruments are constructed from the global variables as weighted averages. The proposed estimation strategy is illustrated using estimates of the NKPC for 8 developed industrial countries. The GVAR generates global factors that are valid instruments and help alleviate the weak instrument problem. The steady states also reflect global in‡uences and any longrun theoretical relationships that might prevail within and across countries in the global economy. The GVAR measure of the steady state performed better than the HP measure, and the use of foreign instruments substantially increased the precision of the estimates of the output coeffcient.
Key Words: Steady States, Long Horizon Expectations, Global VAR, identification, New Keynesian Phillips Curve, TrendCycle Decomposition.
JEL Classifications: C32, E17, F37, F42.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/JMCB08298.pdf
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/JMCB08298data_Individual_Excel_Worksheets.xls
Data Notes: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/JMCB08298datanote.doc

Response to discussions on "Forecasting Economic and Financial Variables with Global VARs", by M. Hashem Pesaran, T. Schuermann and L. Vanessa Smith (2009), International Journal of Forecasting, vol 25, issue 4, pp. 703715

Full Text: http://dx.doi.org/10.1016/j.ijforecast.2009.09.001

"Forecasting Economic and Financial Variables with Global VARs", by M. Hashem Pesaran, T. Schuermann and L. Vanessa Smith (2009), International Journal of Forecasting, vol 25, issue 4, pp. 642675

Abstract: This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate outofsample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the doubleaveraged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.
Key Words: Forecasting using GVAR; Structural breaks and forecasting; Average forecasts across models and windows; Financial and macroeconomic forecasts
JEL Classifications: C32, C51, C53.
Full Text: http://dx.doi.org/10.1016/j.ijforecast.2009.08.007
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/PSS_Supplement_31July09.pdf
Rejoinder: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/PSS_Rejoinder_31July2009.pdf
Data for PSS Paper: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/Data_and_Codes_For_PSS_Paper.zip
Data for PSS Rejoinder: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp09/Data_and_Codes_For_PSS_Rejoinder.zip

"Obituary in Memory of Clive Granger: An Advisory Board Mmember of The Journal", by David F. Hendry and M. Hashem Pesaran (2009), Journal Of Applied Econometrics, vol 24, pp 871–873
Abstract: Professor Sir Clive William John Granger, Kt, Professor Emeritus at the University of California, San Diego, died on 27 May 2009. He was born on 4 September 1934. In a distinguished career spanning more than 50 years, Clive Granger greatly influenced the theory and practice of timeseries econometrics, with major contributions to most of the key concepts and approaches during that period. It is almost impossible to undertake empirical analyses of economic time series without using some of his methods or ideas which spanned causality, spurious regressions, forecasting, longmemory, nonlinearity, aggregation and, most importantly, cointegration, where his formulation with Robert F. Engle, with whom he was awarded the Sveriges Riksbank Prize in Economic Science in Memory of Alfred Nobel in October 2003, changed forever our understanding of nonstationary data.
JEL Classifications:
Key Words:
Full Text: http://www3.interscience.wiley.com/cgibin/fulltext/122605331/PDFSTART

"Pairwise Tests of Purchasing Power Parity", by M. Hashem Pesaran, Ron P. Smith, Takashi Yamagata, Lyudmyla Hvozdyk (2009), Econometric Reviews, vol 28, pp 495521
Abstract: Given nominal exchange rates and price data on N + 1 countries indexed by i = 0,1,2,…, N, the standard procedure for testing purchasing power parity (PPP) is to apply unit root or stationarity tests to N real exchange rates all measured relative to a base country, 0, often taken to be the U.S. Such a procedure is sensitive to the choice of base country, ignores the information in all the other crossrates and is subject to a high degree of crosssection dependence which has adverse effects on estimation and inference. In this article, we conduct a variety of unit root tests on all possible N(N + 1)/2 real rates between pairs of the N + 1 countries and estimate the proportion of the pairs that are stationary. This proportion can be consistently estimated even in the presence of crosssection dependence. We estimate this proportion using quarterly data on the real exchange rate for 50 countries over the period 19572001. The main substantive conclusion is that to reject the null of no adjustment to PPP requires sufficiently large disequilibria to move the real rate out of the band of inaction set by trade costs. In such cases, one can reject the null of no adjustment to PPP up to 90% of the time as compared to around 40% in the whole sample using a linear alternative and almost 60% using a nonlinear alternative..
JEL Classifications: C23, F31, F41
Key Words: Crossrates; Crosssection dependence; Pairwise approach; Panel data; Purchasing power parity.
Full Text: http://www.tandfonline.com/doi/abs/10.1080/07474930802473702
Supplement: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/SupplementMarch06.pdf
Gauss Code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp2007/Pairwise11July08.zip

"The Iranian Economy in the Twentieth Century: A Global Perspective", by Hadi Salehi Esfahani and M. Hashem Pesaran (2009), Iranian Studies, vol 42, issue 2, pp 177211
Abstract: This paper examines the transformation of the Iranian economy through the twentieth century within a global context. At the start of that century, the Iranian economy had long remained stagnant, poor, and largely agrarian, with a marginal role in the world economy. By the turn of the twentyfirst century, Iran had transformed into a complex and relatively large economy with important consequences for the economies of the Middle East and other parts of the world. While the initial conditions and the evolution of domestic institutions and resources played major roles in the pace and nature of that transformation, relations with the rest of the world had crucial influences as well. This paper focuses on the latter forces, while taking account of their interactions with domestic factors in shaping the particular form of economic development in Iran. We study the ways in which the development of the Iranian economy has been affected by international price movements and by the ebbs and flows of trade, investment, and economic growth in the rest of the world. In considering these effects, we also analyze the role of domestic political economy factors and policies in enhancing or hindering the ability of domestic producers to respond to external challenges and opportunities.
JEL Classifications: N15, O11, O53.
Key Words: Development and Growth, Political Economy, Oil Prices and the Iranian Economy.
Full Text: http://www.informaworld.com/smpp/content~content=a910165424~db=all~jumptype=rss
Translation into Persian: by Ali Sarzeem in Donyae Eqtesad http://www.donyaeeqtesad.com/Default_view.asp?@=155302

"Testing Dependence Among Serially Correlated Multicategory Variables", by M. Hashem Pesaran and Allan Timmermann (2009), Journal of the American Statistical Association, vol 104, no 485, pp 325337
Abstract: The contingency table literature on tests for dependence among discrete multicategory variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist on the effect of serial dependency  a problem that is important in areas such as economics, finance, medical trials, and meteorology. This article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests allow for an arbitrary number of categories as well as multiway tables of arbitrary dimension and are robust in the presence of serial dependencies that take the form of finiteorder Markov processes. For threeway or higher order tables we propose new tests of joint and marginal independence. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to microeconomic survey data on firms’ forecasts of changes to their production and prices demonstrates the importance of correcting for serial dependencies in predictability tests.
JEL Classifications:
Key Words: Canonical correlations; Contingency tables; Markov chains; Serial dependence.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp09/PesaranTimmermannjasa.2009.pdf
Supplementary Material : http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp09/PesaranTimmermannSupplement_JASA_Oct_11_2008.pdf

"Model Averaging in Risk Management with an Application to Futures Markets", by M. Hashem Pesaran, Christoph Schleicher and Paolo Zaffaroni (2009), Journal of Empirical Finance, vol 16, issue 2, pp 280305
Abstract: This paper considers the problem of model uncertainty in the case of multiasset volatility models and discusses the use of model averaging techniques as away of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple ValueatRisk (VaR) diagnostic test is proposed for individual as well as ‘average’ models. The asymptotic as well as the exact finitesample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991–2007. The empirical evidence supports the use of ‘thick’ model averaging strategies over single models or Bayesian type model averaging procedures.
JEL Classifications: C32, C52, C53, G11.
Key Words: Model averaging, ValueatRisk, Decision based evaluations.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp09/EMPFIN400.pdf
Matlab Code for PSZ MARM: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp08/MatlabcodeforPSZ_MARM.zip
Documentation of Matlab Code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp08/MARMMatlabcode.pdf

"Nonnested hypotheses", by Rodrigo Dupleich Ulloa and M. Hashem Pesaran, (2008), The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan. The New Palgrave Dictionary of Economics Online.
Palgrave Macmillan.
Abstract: This article provides an overview of the literature on hypotheses testing when the hypotheses or models under consideration are nonnested. Two models are said to be nonnested if neither can be obtained from the other by some limiting process, including the imposition of equality and/or inequality constrains on one of the model's parameters. Relevant concepts such as closeness measures and pseudotrue values are discussed and alternative approaches to testing nonnested hypotheses, including the Cox procedure, artificial nesting and the encompassing approach, are reviewed. The Vuong approach to model selection is also covered.
JEL Classifications:
Key Words: artificial nesting; Cox's test; encompassing test; hypothesis testing; Kullback–Leibler information criteria; linear regression models; maximum likelihood estimation; model selection; nonnested hypotheses; pseudotrue values
Full Text: http://www.dictionaryofeconomics.com/article?id=pde2008_N000084&goto=nonnested&result_number=1225

"Econometrics", by John Geweke, Joel Horowitz, and M. Hashem Pesaran, (2008), The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan. The New Palgrave Dictionary of Economics Online.
Palgrave Macmillan.
Abstract: As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly. Major advances have taken place in the analysis of crosssectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take it into account either by integrating out its effects or by modelling the sources of heterogeneity when suitable panel data exist. The counterfactual considerations that underlie policy analysis and treatment valuation have been given a more satisfactory foundation. New timeseries econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Nonlinear econometric techniques are used increasingly in the analysis of crosssection and timeseries observations. Applications of Bayesian techniques to econometric problems have been promoted largely by advances in computer power and computational techniques. The use of Bayesian techniques has in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process, thus providing a basis for ‘real time econometrics’.
JEL Classifications: C1, C2, C3, C4, C5.
Key Words: History of econometrics, Microeconometrics, Macroeconometrics, Bayesian Econometrics, Nonparametric and Semiparametric Analysis.
Full Text: http://www.dictionaryofeconomics.com/article?id=pde2008_E000007&goto=E&result_number=437

"A BiasAdjusted Lm Test Of Error Cross Section Independence", by M. Hashem Pesaran, Aman Ullah and Takashi Yamagata, (2008), The Econometrics Journal, vol 11, pp. 105–127
Abstract: This paper proposes biasadjusted normal approximation versions of Lagrange multiplier (NLM) test of error cross section independence of Breusch and Pagan (1980) in the case of panel models with strictly exogenous regressors and normal errors. The exact mean and variance of the Lagrange multiplier (LM) test statistic are provided for the purpose of the biasadjustments, and it is shown that the proposed tests have a standard normal distribution for the fixed time series dimension (T) as the cross section dimension (N) tends to infinity. Importantly, the proposed biasadjusted NLM tests are consistent even when the Pesaran’s (2004) CD test is inconsistent. Also alternative biasadjusted NLM tests, which are consistent under local error cross section independence of any fixed order p, are proposed. The finite sample behavior of the proposed tests are investigated and compared to the LM, NLM, and CD tests. It is shown that the biasadjusted NLM tests successfully control the size, maintaining satisfactory power in panel with exogenous regressors and normal errors, even when cross section mean of the factor loadings is close to zero, where the CD test has little power. However, it is also shown that the biasadjusted NLM tests are not as robust as the CD test to nonnormal errors and/or in the presence of weakly exogenous regressors.
JEL Classifications: C12, C13, C33
Key Words: Cross Section Dependence, Spatial Dependence, LM test, Panel Model, Biasadjusted Test
Full Text: http://www.blackwellsynergy.com/doi/abs/10.1111/j.1368423X.2007.00227.x

"Testing Slope Homogeneity In Large Panels", by M. Hashem Pesaran and Takashi Yamagata, (2008), Journal of Econometrics, vol 142, pp. 5093
Abstract: This paper proposes a standardized version of Swamy’s test of slope homogeneity for panel data models where the cross section dimension (N) could be large relative to the time series dimension (T). The proposed test, denoted by , exploits the cross section dispersion of individual slopes weighted by their relative precision. In the case of models with strictly exogenous regressors, but with nonnormally distributed errors, the test is shown to have a standard normal distribution as such that When the errors are normally distributed, a meanvariance bias adjusted version of the test is shown to be normally distributed irrespective of the relative expansion rates of N and T. The test is also applied to stationary dynamic models, and shown to be valid asymptotically so long as N/T → κ , as where 0 ≤ κ ∞ . Using Monte Carlo experiments, it is shown that the test has the correct size and satisfactory power in panels with strictly exogenous regressors for various combinations of N and T. Similar results are also obtained for dynamic panels, but only if the autoregressive coefficient is not too close to unity and so long as T ≥ N.
JEL Classifications: C12, C33.
Keywords: Testing Slope Homogeneity, Dispersion Tests, Large Panels, Monte Carlo Results.
Full Text: http://www.sciencedirect.com/science/article/B6VC04NT84WG1/2/0eb602a751c2c2f54843fff6ecf83ae0
Code and Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/ppfiles/Deltatest.zip

"Econometric Analysis of Structural Systems with Permanent and Transitory Shocks", by Adrian Pagan and M. Hashem Pesaran (2008), Journal of Economic Dynamics and Control, vol 32, issue 10, pp. 33763395
Abstract: This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah (1989), and shows that structural equations with known permanent shocks can not contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a reexamination of the identification schemes used by Wickens and Motto (2001), Shapiro and Watson (1988), King, Plosser, Stock, Watson (1991), Gali (1992, 1999) and Fisher (2006).
JEL Classifications: C30, C32, E10.
Key Words: Permanent shocks, structural identification, error correction models, ISLM models.
Full Text: http://dx.doi.org/10.1016/j.jedc.2008.01.006

"Firm Heterogeneity And Credit Risk Diversification" by Samuel Hanson, M. Hashem Pesaran, and Til Schuermann, (2008), Journal of Empirical Finance, vol 15, issue 4, pp. 583612.
Abstract: This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to under estimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is controlled for, the impact of neglecting parameter heterogeneity is complex and depends on the source and degree of heterogeneity. We show that ignoring differences in default thresholds results in overestimation of risk, while ignoring differences in return correlations yields ambiguous results. Our empirical application, designed to be typical and representative, combines both and shows that neglected heterogeneity results in overestimation of risk. Using a portfolio of U.S. firms we illustrate that heterogeneity in the default threshold or probability of default, measured for instance by a credit rating, is of first order importance in affecting the shape of the loss distribution: including ratings heterogeneity alone results in a 20% drop in loss volatility and a 40% drop in 99.9% VaR, the level to which the risk weights of the New Basel Accord are calibrated.
JEL Classifications: C33, G13, G21.
Keywords: Risk management, correlated defaults, factor models, portfolio choice.
Full text: http://dx.doi.org/10.1016/j.jempfin.2007.11.002

"Random Coefficient Models", Cheng Hsiao and M. Hashem Pesaran, (2008), L. Matyas, and P. Sevestre (eds), The Econometrics of Panel Data (Third Edition), Ch 6, pp. 185213.
Abstract: This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneous dynamic panels, testing for homogeneity under weak exogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of crosssectional dependence in panel data models.
JELClassification: C12, C13, C33.
Keywords: Random coefficient models, Dynamic heterogeneous panels, Classical and Bayesian approaches, Tests of slope heterogeneity, Cross section dependence.
Full text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/HsiaoPesaranRCM20July07.pdf

"Unit Roots and Cointegration in Panels", by Breitung, J. and M.H. Pesaran (2008), L. Matyas, and P. Sevestre (eds), The Econometrics of Panel Data (Third Edition), Ch 9, pp. 279322.
Abstract: This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.
JEL Classification: C12, C15, C22, C23.
Keywords: Panel Unit Roots, Panel Cointegration, Cross Section Dependence, Common Effects
Full text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp2007/panelUnitCoin_final.pdf

"Forecasting the Swiss Economy Using VECX* Models: An Exercise in Forecast Combination Across Models and Observation Windows", Katrin AssenmacherWesche and M. Hashem Pesaran, (2008), National Institute Economic Review 203, 91–108.
Abstract: This paper uses vector error correction models of Switzerland for forecasting output, inflation and the shortterm interest rate. It considers three different ways of dealing with forecast uncertainties. First, it investigates the effect on forecasting performance of averaging over forecasts from different models. Second, it considers averaging forecasts from different estimation windows. It is found that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, it examines whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of alternative weighting schemes on forecast accuracy is small in the present application.
JEL Classifications: C53, C32
Key Words: Bayesian Model averaging, Choice of Observation Window, Longrun Structural Vector Autoregression.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/fp2007/SwissForecastingNIESR17Dec07.pdf

"Long Run Macroeconomic Relations In The Global Economy", by Stephane Dees, Sean Holly, M. Hashem Pesaran and L. Vanessa Smith, economics  The OpenAccess, OpenAssessment EJournal, 20073.
Abstract: This paper focuses on testing long run macroeconomic relations for interest rates, equity, prices and exchange rates within a model of the global economy. It considers a number of plausible long run relationships suggested by arbitrage in financial and goods markets, and uses the global vector autoregressive (GVAR) model developed in Dees, di Mauro, Pesaran and Smith (2007) to test for long run restrictions in each country/region conditioning on the rest of the world. Bootstrapping is used to compute both the empirical distribution of the impulse responses and the loglikelihood ratio statistic for overidentifying restrictions. The paper also examines the speed with which adjustments to the long run relations take place via the persistence profiles. We find strong evidence in favour of the uncovered interest parity and to a lesser extent the Fisher equation across a number of countries, but our results for the PPP are much weaker. Also as to be expected, the transmission of shocks and subsequent adjustments in financial markets are much faster than those in goods markets.
JEL Classifications: C32, E17, F47, R11.
Key Words: Global VAR, interdependencies, Fisher relationship, Uncovered Interest Rate Parity , Purchasing Power Parity, persistence profile, error variance decomposition.
Full Text: http://www.economicsejournal.org/economics/journalarticles/20073

"A Simple Panel Unit Root Test In The Presence Of Cross Section Dependence" by M. Hashem Pesaran, in Journal of Applied Econometrics, Vol 22, Issue 2, pp. 265312, 2007.
Abstract: A number of panel unit root tests that allow for cross section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross dependence of the series before standard panel unit root tests are applied to the transformed series. In this paper we propose a simple alternative where the standard ADF regressions are augmented with the cross section averages of lagged levels and firstdifferences of the individual series. New asymptotic results are obtained both for the individual cross sectionally augmented ADF (CADF) statistics, and their simple averages. It is shown that the individual CADF statistics are asymptotically similar and do not depend on the factor loadings. The limit distribution of the average CADF statistic is shown to exist and its critical values are tabulated. Small sample properties of the proposed test are investigated by Monte Carlo experiments. The proposed test is applied to a panel of 17 OECD real exchange rate series as well as to log real earnings of households in the PSID data.
JEL Classification: C12, C15, C22, C23.
Key Words: Panel Unit Root Tests, Cross Section Dependence, Monte Carlo Results, Purchasing Power Parity, Real Earning Dynamics.
Full text: http://www3.interscience.wiley.com/cgibin/abstract/114211628/ABSTRACT
Gauss Codes for Computation of the CADF Panel Unit Root Test Statistics: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pub2007/CADFgauss6.zip

"Learning, Structural Instability And Present Value Calculations" by M. Hashem Pesaran, Davide Pettenuzzo and Allan Timmermann, in Econometric Reviews, Vol 26, Issue 24, pp. 253288, 2007
Abstract: Present value calculations require predictions of cash flows both at near and distant future points in time. Such predictions are generally surrounded by considerable uncertainty and may critically depend on assumptions about parameter values as well as the form and stability of the data generating process underlying the cash flows. This paper presents new theoretical results for the existence of the infinite sum of discounted expected future values under uncertainty about the parameters characterizing the growth rate of the cash flow process. Furthermore, we explore the consequences for present values of relaxing the stability assumption in a way that allows for past and future breaks to the underlying cash flow process. We find that such breaks can lead to considerable changes in present values.
JEL Classifications: C11, G12, G22
Key Words: Bayesian learning; Present value; Stock prices; Structural breaks.
Full Text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pub2007/LECR_A_221955_O.pdf

"A PairWise Approach To Testing For Output And Growth Convergence" by M. Hashem Pesaran, in the Journal of Econometrics, Vol 138, Issue 1, pp. 312355, 2007
Abstract: This paper proposes a pairwise approach to testing for output con vergence that considers all N(N  1)/2 possible pairs of log per capita output gaps across N economies. A general probabilistic definition of output convergence is also proposed, which suggests that all such out put gap pairs must be stationary with a constant mean. The approach is compatible with individual output series having unit roots, or other nonstationary common components and does not involve the choice of a reference country in computation of output gaps. It is also applicable when N is large relative to T (the time dimension of the panel). After providing some encouraging Monte Carlo evidence on the small sample properties of the pairwise test, the test is applied to output series in the Penn World Tables over 19502000. Overall, the results do not support output convergence, and suggest that the findings of convergence clubs in the literature might be spurious. However, significant evidence of growth convergence is found, a result which is reasonably robust to the choice of the sample period and country groupings. Nonconvergence of log per capita outputs combined with growth convergence suggests that while common technological progress seems to have been diffusing reasonably widely across economies, there are nevertheless important countryspecific factors that render output gaps highly persistent, such that we can not be sure that the probability for the output gaps to lie within a fixed range will be nonzero.
JEL Classifications: C32, C33, D90, O47
Keywords: Economic Growth, Panel Data Models, Common Technological Shocks, Convergence.
Full text: http://dx.doi.org/10.1016/j.jeconom.2006.05.024

"Exploring The International Linkages Of The Euro Area: A Global Var Analysis" by Stephane Dees, Filippo di Mauro, M. Hashem Pesaran, and L. Vanessa Smith, in the Journal of Applied Econometrics, Vol 22, Issue 1, pp.138, 2007.
Abstract: This paper presents a quarterly global model combining individual country vector errorcorrecting models in which the domestic variables are related to the countryspecific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy, over the period 19792003. It advances research in this area in a number of directions. In particular, it provides a theoretical framework where the GVAR is derived as an approximation to a global unobserved common factor model. Using average pairwise crosssection error correlations, the GVAR approach is shown to be quite effective in dealing with the common factor interdependencies and international comovements of business cycles. It develops a sieve bootstrap procedure for simulation of the GVAR as a whole, which is then used in testing the structural stability of the parameters, and for establishing bootstrap confidence bounds for the impulse responses. Finally, in addition to generalized impulse responses, the current paper considers the use of the GVAR for ‘structural’ impulse response analysis. Although, the GVAR model can be used for many different purposes, this paper focusses on the short term and long term implications of external shocks for the euro area economy, particularly in response to shocks to the U.S.. The results show that financial shocks are transmitted rapidly, and often get amplified as they travel from the U.S. to the euro area. Equity and bond markets seem to be far more synchronous as compared to real output and inflation. While the impact of an oil price shock on inflation is statistically significant, its impact on output remains limited. In contrast, the effects of a shock to the U.S. monetary policy for the euro area output and inflation are limited and not highly significant.
JEL Classifications: C32, E17, F47
Keywords: Global VAR (GVAR), Global interdependencies, global macroeconomic modeling, impulse responses.
Full text: http://www3.interscience.wiley.com/journal/114185783/abstract
Supplement A: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/SupplementA(Data&Bootstrap)DdPS10Dec06.pdf
Supplement B: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/SupplementB(Additional Results)DdPS11April06.pdf
Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/Data.zip
GVAR code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/GVAR.zip

"Econometric Issues In The Analysis Of Contagion", by M. Hashem Pesaran and Andreas Pick, in the Journal of Economic Dynamics and Control, Vol 31, Issue 4, pp. 12451277, 2007.
Abstract: This paper presents a canonical, econometric model of contagion and investigates the conditions under which contagion can be distinguished from interdependence. In a twomarket set up it is shown that for a range of fundamentals the solution is not unique, and for sufficiently large values of the contagion coefficients it has interesting bifurcation properties with bimodal density functions. The identification of contagion requires that the equations for the individual markets contain market specific regressors. This sheds doubt on the general validity of the correlation based tests of contagion recently proposed in the literature which do not involve any market specific variables. Furthermore, we show that ignoring endogeneity and interdependence can introduce an upward bias in the estimate of the contagion coefficient, and using Monte Carlo experiments we further show that this bias could be substantial. Finally, we analyse data on European interest rates spreads during the ERM and find a clear asymmetry in the contagion effects of sharp rises and falls; with only the former having some statistically significant effects.
JEL Classifications: C10, C123, G10, G15
Keywords: Contagion, Interdependence, Identification, Financial Crises.
Full text: http://dx.doi.org/10.1016/j.jedc.2006.03.008

"Selection Of Estimation Window In The Presence Of Breaks", by M. Hashem Pesaran and Allan Timmermann, Journal of Econometrics,Vol. 137, Issue 1, pp. 134161, 2007.
Abstract: In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use prebreak data to estimate the parameters of the model used to compute outofsample forecasts. The issue of how best to exploit the tradeoff that might exist between bias and forecast error variance is explored and illustrated for the multivariate regression model under the assumption of strictly exogenous regressors. In practice when this assumption cannot be maintained and both the time and size of the breaks are unknown the optimal choice of the observation window will be subject to further uncertainties that make exploiting the biasvariance tradeoff difficult. To that end we propose a new set of crossvalidation methods for selection of a single estimation window and weighting or pooling methods for combination of forecasts based on estimation windows of different lengths. Monte Carlo simulations are used to show when these procedures work well compared with methods that ignore the presence of breaks.
JEL Classifications: C22, C53.
Key Words: Parameter instability, forecasting with breaks, choice of observation window, forecast combination
Full Text: http://dx.doi.org/10.1016/j.jeconom.2006.03.010

"What if the UK or Sweden had Joined the Euro in 1999? An Empirical Evaluation using a Global VAR", by M. Hashem Pesaran, L. Vanessa Smith, Ron P. Smith, International Journal of Finance and Economics, Vol. 12, Issue 1, pp. 5587, 2007.
Abstract: This paper attempts to provide a conceptual framework for the analysis of counterfactual scenarios using macroeconometric models. As an application we consider UK entry to the euro. Entry involves a longterm commitment to restrict UK nominal exchange rates and interest rates to be the same as those of the euro area. We derive conditional probability distributions for the difference between the future realisations of variables of interest (e.g UK and euro area output and prices) subject to UK entry restrictions being fully met over a given period and the alternative realisations without the restrictions. The robustness of the results can be evaluated by also conditioning on variables deemed to be invariant to UK entry, such as oil or US equity prices. Economic interdependence means that such policy evaluation must take account of international linkages and common factors that drive fluctuations across economies. In this paper this is accomplished using the Global VAR recently developed by Dees, di Mauro, Pesaran and Smith (2005). The paper briefly describes the GVAR which has been estimated for 25 countries and the euro area over the period 19792003. It reports probability estimates that output will be higher and prices lower in the UK and the euro area as a result of entry. It examines the sensitivity of these results to a variety of assumptions about when and how the UK entered and the observed global shocks and compares them with the effects of Swedish entry.
JEL Classifications: C32, C35, E17, F15, F42.
Keywords: Global VAR (GVAR), Counterfactual Analysis, UK and Sweden entry to euro.
Full text: https://onlinelibrary.wiley.com/doi/abs/10.1002/ijfe.312
Code and Data: Euroentry_Code_20May2006.zip

"Alternative Approaches To Estimation And Inference In Large Multifactor Panels: Small Sample Results With An Application To Modelling Of Asset Return". By G. Kapetanios and M. Hashem, Pesaran, in Garry Phillips and Elias Tzavalis (eds.), The Refinement of Econometric Estimation and Test Procedures: Finite Sample and Asymptotic Analysis, Cambridge University Press, Cambridge, 2007.
Abstract: This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran, and the other uses principal components following the work of Stock and Watson. The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.
Keywords: Cross Section Dependence, Large Panels, Principal Components, Common Correlated Effects, Return Equations.
JELClassification: C12, C13, C33.
Full text: http://www.cambridge.org/catalogue/catalogue.asp?isbn=0521870534

"Global Business Cycles And Credit Risk", by M. Hashem Pesaran, Til Schuermann, and BjörnJakob Treutler, in ' The Risks of Financial Institutions ', Mark Carey and Rene M. Stultz (eds.), 2006, Ch. 9, pp. 419473, with Comment by Richard Cantor. ISBN No.'s: 13: 9780226092850 & 10: 0226092852
Abstract: The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconometric model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firmlevel parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fattailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.
Also available at: Wharton Financial Institutions Center WP #0514
Key Words: Risk management, default dependence, economic interlinkages, portfolio choice
JEL Classifications: C32, E17, G20

"Forecasting Time Series Subject To Multiple Structural Breaks". By M. Hashem Pesaran, Davide Pettenuzzo, and Allan Timmermann, Review of Economic Studies, 2006, October, Vol. 73, Issue 4, pp. 10571084
Abstract: This paper provides a new approach to forecasting time series that are subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks occurring over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the parameters from the meta distribution that characterizes the stochastic break point process. In an application to US Treasury bill rates, we find that the compositemeta method leads to better outofsample forecasts than a range of alternative methods.
JEL Classifications: C110, C150, C530.
Keywords: Structural Breaks, Forecasting, Hierarchical hidden Markov Chain Model, Bayesian Model Averaging.
Journal: https://academic.oup.com/restud/article/73/4/1057/1573279

"Macroeconomic Dynamics And Credit Risk: A Global Perspective", M. Hashem Pesaran, Til Schuermann, BjornJakob Treutler, Scott M.Weiner, Journal of Money Credit and Banking, Volume 38, No. 5, pp. 12111262 , August 2006.
Abstract: This paper presents a new approach to modelling conditional credit loss distributions. Asset value changes of firms in a credit portfolio are linked to a dynamic global macroeconometric model, allowing macro effects to be isolated from idiosyncratic shocks from the perspective of default (and hence loss). Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. We allow for firmspecific business cycle effects and the heterogeneity of firm default thresholds using credit ratings. The model can be used, for example, to compute the effects of a hypothetical negative equity price shock in South East Asia on the loss distribution of a credit portfolio with global exposures over one or more quarters. We show that the effects of such shocks on losses are asymmetric and nonproportional, reflecting the highly nonlinear nature of the credit risk model.
JEL Classifications: C32, E17, G20
Key Words: Risk management, economic interlinkages, loss forecasting, default correlation
Journal: http://dx.doi.org/10.1353/mcb.2006.0074

"Market Efficiency Today", by M. Hashem Pesaran , Medium For Econometric Applications, Vol. 14, Issue 2, Spring 2006.
Abstract: Economists have long been fascinated by the sources of variations in the stock market. By the early 1970’s a consensus had emerged among financial economists suggesting that stock prices could be well approximated by a random walk model and that changes in stock returns were basically unpredictable. Fama (1970) provides an early, definitive statement of this position. Historically, the ‘random walk’ theory of stock prices was preceded by theories relating movements in the financial markets to the business cycle. A prominent example is the interest shown by Keynes in the variation in stock returns over the business cycle. The efficient market hypothesis (EMH) evolved in the 1960’s from the random walk theory of asset prices advanced by Paul Samuelson (1965). Samuelson showed that in an informationally efficient market price changes must be unforecastable. Kendall (1953), Cowles (1960), Osborne (1959, 1962), and many others had already provided statistical evidence on the random nature of equity price changes. Samuelson’s contribution was, however, instrumental in providing academic respectability for the hypothesis, despite the fact that the random walk model had been around for many years; having been originally discovered by Louis Bachelier, a French statistician, back in 1900!
JEL Classifications:
Key Words:
CFS WP: https://www.ifkcfs.de/fileadmin/downloads/publications/wp/06_01.pdf

"Macroeconomic Modelling With A Global Perspective" by M. Hashem Pesaran and Ron Smith , The Manchester School, Supplement, 2006. pp. 2449.
Abstract: This paper provides a synthesis and further development of a global modelling approach introduced in Pesaran, Schuermann andWeiner (2004), where country specific models in the form of VARX* structures are estimated relating a vector of domestic variables, x_{it}, to their foreign counterparts, x_{it}, and then consistently combined to form a Global VAR (GVAR). It is shown that the VARX* models can be derived as the solution to a dynamic stochastic general equilibrium (DSGE) model where overidentifying longrun theoretical relations can be tested and imposed if acceptable. This gives the system a transparent longrun theoretical structure. Similarly, shortrun overidentifying theoretical restrictions can be tested and imposed if accepted. Alternatively, if one has less confidence in the shortrun theory the dynamics can be left unrestricted. The assumption of the weak exogeneity of the foreign variables for the longrun parameters can be tested, where x*_{it} variables can be interpreted as proxies for global factors. Rather than using deviations from ad hoc statistical trends, the equilibrium values of the variables reflecting the longrun theory embodied in the model can be calculated. The paper also provides some new results on the relative importance of external shocks for the UK and the euro area economies.
JEL Classifications: C32, E17, F37, F42.
Key Words: Global VAR (GVAR), DSGE models, VARX*
Full Text: http://www.blackwellsynergy.com/doi/abs/10.1111/j.14679957.2006.00516.x

"Estimation And Inference In Large Heterogeneous Panels With A Multifactor Error Structure", by M. Hashem Pesaran, Econometrica 74 (4), 9671012.
Abstract: This paper presents a new approach to estimation and inference in panel data models with a general multifactor error structure. The unobserved factors and the individualspecific errors are allowed to follow arbitrary stationary processes, and the number of unobserved factors need not be estimated. The basic idea is to filter the individualspecific regressors by means of crosssection averages such that asymptotically as the crosssection dimension (N) tends to infinity the differential effects of unobserved common factors are eliminated. The estimation procedure has the advantage that it can be computed by least squares applied to auxiliary regressions where the observed regressors are augmented with cross sectional averages of the dependent variable and the individualspecific regressors. A number of estimators (refereed to as common correlated effects, CCE, estimators) are proposed and their asymptotic distributions are derived. The small sample properties of mean group and pooled CCE estimators are investigated by Monte Carlo experiments, showing that the CCE estimators have satisfactory small sample properties even under a substantial degree of heterogeneity and dynamics and for relatively small values of N and T.
JELClassification: C12, C13, C33.
Keywords: Cross Section Dependence, Large Panels, Common Correlated Effects, Heterogeneity, Estimation and Inference.
Full Text: http://www.blackwellsynergy.com/doi/abs/10.1111/j.14680262.2006.00692.x
Gauss Code: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/ppfiles/CCEgauss6_22Aug08.zip

"Survey Expectations", by M. Hashem Pesaran and Martin Weale, in the Handbook of Economic Forecasting, G. Elliott, C.W.J. Granger, and A.Timmermann (eds.), NorthHolland .
Abstract: This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. Other models of expectations formation, such as the adaptive expectations hypothesis, are briefly discussed. Testable implications of rational and extrapolative models of expectations are reviewed and the importance of the loss function for the interpretation of the test results is discussed. The paper then provides an account of the various surveys of expectations, reviews alternative methods of quantifying the qualitative surveys, and discusses the use of aggregate and individual survey responses in the analysis of expectations and for forecasting.
JEL Classifications: C40, C50, C53, C80
Key Words: Models of Expectations Formation, Survey Data, Heterogeneity, Tests of Rational Expectations.
Full text: http://dx.doi.org/10.1016/S15740706(05)010141

SMALL SAMPLE PROPERTIES OF FORECASTS FROM AUTOREGRESSIVE MODELS UNDER STRUCTURAL BREAKS, by M. Hashem Pesaran, Allan Timmermann. Journal of Econometrics, 129, pp. 183217.
Abstract: This paper develops a theoretical framework for the analysis of smallsample properties of forecasts from general autoregressive models under structural breaks. Finitesample results for the mean squared forecast error of onestep ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks.
JEL Classifications: C22, C53.
Key Words: Small sample properties of forecasts, MSFE, structural breaks, autoregression, rolling window estimator.
Full text: http://dx.doi.org/10.1016/j.jeconom.2004.09.007

THE COST EFFECTIVENESS OF THE UK'S SOVEREIGN DEBT PORTFOLIO, by Patrick J. Coe, M. Hashem Pesaran and Shaun P. Vahey, 2005, Oxford Bulletin of Economics and Statistics, 67, pp. 467495.
Abstract: This paper provides a recursive empirical analysis of the scope for cost minimization in public debt management when the debt manager faces a given short term interest rate dictated by monetary policy as well as risk and market impact constraints. It simulates the `real time' interest costs of alternative portfolios for UK government debt between April 1985 and March 2000. These portfolios are constructed using forecasts of return spreads based on a recursive modelling procedure. While we find statistically significant evidence of predictability, the interest cost savings are quite small when portfolio shares are constrained to lie within historical bounds.
JEL Classifications: E17, E44, G12, H63.
Keywords: Public debt management, cost minimization, recursive modelling, data snooping.
Full Text: http://www.blackwellsynergy.com/doi/abs/10.1111/j.14680084.2005.00128.x

ESTIMATION AND INFERENCE IN SHORT PANEL VECTOR AUTOREGRESSIONS WITH UNIT ROOTS AND COINTEGRATION With, Michael Binder and Cheng Hsiao, 2005, Econometric Theory, Volume 21, No.4, pp. 795837.
Abstract: This paper considers estimation and inference in panel vector autoregressions (PVARs) where (i) the individual effects are either random or fixed, (ii) the timeseries properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its crosssectional dimension is large. Generalized Method of Moments (GMM) and Quasi Maximum Likelihood (QML) estimators are obtained and compared in terms of their asymptotic and finite sample properties. It is shown that the asymptotic variances of the GMM estimators that are based on levels as well as firstdifferences of the model variables depend on the variance of the individual effects; whereas by construction the fixed effects QML estimator is not subject to this problem. Monte Carlo evidence is provided showing that the fixed effects QML estimator tends to outperform the various GMM estimators in finite sample under both normal and nonnormal errors. The paper also shows how the fixed effects QML estimator can be successfully used for unit root and cointegration tests in short panels.
JELClassification: C12, C13, C33.
Keywords: Panel Vector Autoregressions, Random/Fixed Effects, Unit Roots, Cointegration.
Full Text: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=318327

REAL TIME ECONOMETRICS. by Hashem Pesaran and Allan Timmermann, Econometric Theory, 2005, 21, pp. 212231.
Abstract: This paper considers the problems facing decision makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision maker's actions to the data generating process is also discussed and highlighted through an example.
JEL Classifications: C51, C52, C53.
Keywords: Specification Search, Data Snooping, Recursive/Sequential Modelling, Automated Model Selection.
Full text: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=281018

"HOW COSTLY IS IT TO IGNORE BREAKS WHEN FORECASTING THE DIRECTION OF A TIME SERIES?" M. Hashem Pesaran and Allan Timmermann, International Journal of Forecasting, Volume, 20, No. 3, JulyAugust 2004, pp. 411425.
Abstract: Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast the sign or direction of a timeseries that is subject to breaks. Our results suggest that it can be very costly to ignore breaks. Forecasting approaches that condition on the most recent break are likely to perform better over unconditional approaches that use expanding or rolling estimation windows provided that the break is reasonably large.
JEL Classification: C22, G10
Key Words: Sign prediction, estimation window, structural breaks.
Full Text: http://dx.doi.org/10.1016/S01692070(03)000682

"MODELLING REGIONAL INTERDEPENDENCIES USING A GLOBAL ERRORCORRECTING MACROECONOMETRIC MODEL", M. Hashem Pesaran, Til Schuermann and Scott Weiner. Journal of Business and Economics Statistics, Volume 22, Number 2, April 2004, pp 129162
Abstract: Financial institutions are ultimately exposed to macroeconomic fluctuations in the global economy. This paper proposes and builds a compact global model capable of generating forecasts for a core set of macroeconomic factors (or variables) across a number of countries. The model explicitly allows for the interdependencies that exist between national and international factors. Individual regionspecific vector errorcorrecting models are estimated, where the domestic variables are related to corresponding foreign variables constructed exclusively to match the international trade pattern of the country under consideration. The individual country models are then linked in a consistent and cohesive manner to generate forecasts for all the variables in the world economy simultaneously. The global model is estimated for 25 countries grouped into 11 regions using quarterly data over 1979Q199Q1. The degree of regional interdependencies is investigated via generalized impulse responses where the effects of shocks to a given variable in a given country on the rest of the world are provided. The model is then used to investigate the effects of various global risk scenarios on a bank's loan portfolio.
JEL Classification: C32, E17, G20.
Key Words: Global interdependencies, global macroeconometric modeling, Credit loss distribution, Risk management, Global Vector Error Correcting Model.
Full Text: http://dx.doi.org/10.1198/073500104000000019
Figures (colour): http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/GVARcolourCharts.pptRejoinder to Comments by Baltagi, Dennis and Lopez, Johansen, and Wallis Journal of Business Economics and Statistics, Volume 22, Number 2, April 2004, pp 175181 http://dx.doi.org/10.1198/073500104000000064

"FORECAST UNCERTAINTIES IN MACROECONOMETRIC MODELLING: AN APPLICATION TO THE UK ECONOMY", Anthony Garratt, Kevin Lee, M. Hashem Pesaran, and Yongcheol Shin, Journal of the American Statistical Association, Applications and Case Studies, December 2003, 98, 464, pp829838
Abstract: This paper argues that probability forecasts convey information on the uncertainties that surround macroeconomic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroeconometric model as well as a number of other alternatives are presented and evaluated using recursive forecasts generated over the period 1999q12001q1. Out of sample probability forecasts of inflation and output growth are also provided over the period 2001q22003q1, and their implications discussed in relation to the Bank of England's inflation target and the need to avoid recessions, both as separate events and jointly. The robustness of the results to parameter and model uncertainties is also investigated using Bayesian model averaging techniques.
JEL Classifications: C32, C53, E17
Keywords: Probability Forecasting, Long Run Structural VARs, Macroeconometric Modelling, Forecast Evaluation, Probability Forecasts of Inflation and Output Growth;
Full text: http://dx.doi.org/10.1198/016214503000000765
Extended Version of the paper: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/ExtendedVersionukprob.pdf

"TESTING FOR UNIT ROOTS IN HETEROGENEOUS PANELS" Kyung So Im, M Hashem Pesaran and Yongcheol Shin, Journal of Econometrics, 115 (July 2003), pp.5374.
Abstract: This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized tbar test statistic based on the (augmented) DickeyFuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) > infinity, followed by N (the cross sectional dimension) > infinity. A diagonal convergence result with T and N > infinity while N/T>k, k being a finite nonnegative constant, is also conjectured. In the special case where errors in individual DickeyFuller (DF) regressions are serially uncorrelated a modified version of the standardized tbar statistic, denoted by Z_{tbar}, is shown to be distributed as standard normal as N> infinity for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough order is selected for the underlying ADF regressions, then the small sample performances of the tbar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (1993).
JEL Classification: C12, C15, C22, C23
Key Words: Heterogeneous dynamic panels, Tests of unit roots, LMbar and tbar statistics, Finite sample properties.
Full Text: http://dx.doi.org/10.1016/S03044076(03)000927

"A LONG RUN STRUCTURAL MACROECONOMETRIC MODEL OF THE UK" Anthony Garratt, Kevin Lee, M. Hashem Pesaran and Yongcheol Shin. Economic Journal, Volume 113, pp. 412455. (April 2003)
Abstract: A new modelling strategy is introduced that provides a practical approach to incorporating longrun structural relationships, suggested by economic theory, in an otherwise unrestricted VAR model. The strategy is applied to construct a small quarterly macroeconometric model of the UK, estimated over 1965q11995q4 in nine variables: domestic and foreign outputs, prices and interest rates, oil prices, the nominal effective exchange rate, and real money balances. The aim is to develop a model with a transparent and theoretically coherent foundation. Tests of restrictions on the longrun relations of the model are presented. The dynamic properties of the model are discussed using impulse responses for the effects of an oil price shock as the movements in interest rates beyond those explained by the implementation of an optimal interest rate rule and by oil price, exchange rate and foreign interest rate innovations.
JEL Classification: C32, E24
Keywords: LongRun Structural VAR, A Core UK Model, Macroeconomic Modelling, Monetary Policy Shock, Oil Price Shock.
Full text: http://www.res.org.uk/journals/abstracts.asp?ref=00130133&vid=113&iid=487&aid=801
Program and Data: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/ukm99.zip

"AGGREGATION OF LINEAR DYNAMIC MODELS: AN APPLICATION TO LIFECYCLE CONSUMPTION MODELS UNDER HABIT FORMATION", M. Hashem Pesaran. Economic Modelling Volume 20, Issue 2, pp 227435 (March 2003) Henry Special Issue
Abstract: This paper provides a general framework for aggregating linear dynamic models by deriving the aggregate model as the optimal prediction (in the minimum meansquared error sense) of the aggregate variable of interest with respect to an aggregate information set generated by current and past values of available aggregate observations. The approach is applied to a number of aggregation problems that have been considered in the literature. It is shown how the results in much of the literature can be readily obtained using the proposed forecasting approach, and a number of important extensions and generalizations are provided. Our approach does not require the assumption of independence of the micro distributed lag coefficients from the other micro coefficients, and establishes that in general the longrun coefficients obtained from the optimal aggregate relation are equal to the averages of the longrun coefficients from the micro relations. The approach is then applied to lifecycle consumption decision rules under habit formation and the implications of the heterogeneity in habit formation coefficients across individuals for the analysis of aggregate consumption is investigated. Using stochastic simulations it is shown that the estimates of the habit persistence coefficient are likely to be biased downward if they are based on analogue aggregate consumption functions, which could partly explain the excess smoothness and excess sensitivity puzzles in terms of neglected heterogeneity.
JEL Classifications: C10, C43, D91, E21
Keywords: Aggregation, Heterogeneous Dynamic Models, Long Memory, Life Cycle Models under Habit Formation.
Full Text: http://dx.doi.org/10.1016/S02649993(02)000597

"MARKET TIMING AND RETURN PREDICTION UNDER MODEL INSTABILITY", M. Hashem Pesaran and Allan Timmermann. Journal of Empirical Finance, 2002, Vol.9 pp.495510.
Abstract: Despite mounting empirical evidence to the contrary, the literature on predictability of stock returns almost uniformly assumes a timeinvariant relationship between state variables and returns. In this paper we propose a new twostage approach for forecasting of financial return series that are subject to breaks. The first stage adopts a reversed ordered Cusum (ROC) procedure to determine in real time when the most recent break has occurred. In the second stage, postbreak data is used to estimate the parameters of the forecasting model. We compare this approach to existing alternatives for dealing with parameter instability such as the BaiPerron method and the timevarying parameter model. An outofsample forecasting experiment demonstrates considerable gains in market timing precision from adopting the proposed twostage forecasting method.
JEL Classification: C22, G10.
Key Words: Predictability of US stock returns. Market timing information. Structural breaks.
Full Text: http://dx.doi.org/10.1016/S09275398(02)000075

“MAXIMUM LIKELIHOOD ESTIMATION OF FIXED EFFECTS DYNAMIC PANEL DATA MODELS COVERING SHORT TIME PERIODS” Cheng Hsiao, M. Hashem Pesaran, A. Kamil Tahmiscioglu. Journal of Econometrics, 2002, Vol.109 pp.107150.
Abstract: A transformed likelihood approach is suggested to estimate fixed effects dynamic panel data models. Conditions on the data generating process of the exogenous variables are given to get around the issue of “incidental parameters”. The maximum likelihood (MLE) and minimum distance estimator (MDE) are suggested. Both estimators are shown to be consistent and asymptotically more efficient than the instrumental variable (IV) or generalized method of moment (GMM) estimators. A Hausman type specification test is suggested to test the fixed versus random effects specification or conditions on the data generating process of the exogenous variables. Monte Carlo studies are conducted to evaluate the finite sample properties of the MLE, MDE, IV, and GMM. It is shown that the likelihood approach appears to dominate the GMM approach both in terms of the bias or root mean squares error of the estimators and the size and power of the test statistics.
JEL Classifications: C13, C15, C23
Key Words: Dynamic Panels, Short Time Periods, Fixed and Random Effects, IV, GMM, Forward Filter, Minimum Distance Estimators, Maximum Likelihood Estimators, Monte Carlo Experiments.
Full text: http://dx.doi.org/10.1016/S03044076(01)001439

"LONG RUN STRUCTURAL MODELLING" M Hashem Pesaran and Yongcheol Shin. Econometrics Reviews, 2002, Vol.21 pp.4987.
Abstract: The paper develops a general framework for identification, estimation, and hypothesis testing in cointegrated systems when the cointegrating coefficients are subject to (possibly) nonlinear and crossequation restrictions, obtained from economic theory or other relevant a priori information. It provides a proof of the consistency of the quasi maximum likelihood estimators (QMLE), establishes the relative rates of convergence of the QMLE of the shortrun and the longrun parameters, and derives their asymptotic distribution; thus generalizing the results already available in the literature for the linear case. The paper also develops tests of the overidentifying (possibly) nonlinear restrictions on the cointegrating vectors. The estimation and hypothesis testing procedures are applied to an Almost Ideal Demand System estimated on U.K. quarterly observations. Unlike many other studies of consumer demand this application does not treat relative prices and real per capita expenditures as exogenously given.
JEL Classifications: C1, C3, D1, E1.
Key Words: Cointegration; identification; QMLE; consistency; asymptotic distribution, testing nonlinear restrictions; Almost Ideal Demand Systems.
Full Text: http://www.informaworld.com/smpp/content~content=a713629093~db=all~order=page

“DECISIONBASED METHODS FOR FORECAST EVALUATION”, M. Hashem Pesaran and Spyros Skouras. In (eds) M.P. Clements and D.F. Hendry, A Companion to Economic Forecasting, Oxford: Basil Blackwell, chapter 11, pp.241267. ISBN 0 631 21569 7.
Abstract: This chapter provides an overview of quantitative and qualitative methods for evaluating forecasts when there exists a priori information regarding the use to which the forecasts will be put. The chapter discusses a decisionbased approach for evaluation and comparison of forecasts, and shows how such an approach can provide a unifying theme for recent developments in the forecast evaluation literature  namely the use of generalized cost of error functions, probability event and density forecast evaluation and the evaluation of markettiming skills. The approach is illustrated by means of a number of simple examples. For forecast comparisons, the chapter discusses the use of the sample mean of the lossdifferentials between using one forecast distribution relative to another. The problem of testing the “equivalence” of two forecast distributions in a decisionbased context is also addressed briefly.
JEL Classification: C44, C52, C53.
Key Words: Forecast evaluation, decision making, economic value of forecasts, probability event forecasts, density forecasts, generalised cost of error functions, lossdifferentials.
Web Link: http://www.blackwellpublishing.com/book.asp?ref=9780631215691

“NONNESTED HYPOTHESIS TESTING: AN OVERVIEW” M. Hashem Pesaran and Melvyn Weeks. In (ed) Badi H Baltagi, Companion to Theoretical Econometrics, (2001) Basil Blackwell, Oxford. ISBN 0 63121 254 X.
Abstract: In econometric analysis nonnested models arise naturally when rival economic theories are used to explain the same phenomenon such as unemployment, inflation or output growth. We examine the problem of hypothesis testing when the models under consideration are “nonnested” or belong to “separate” families of distributions in the sense that none of the individual models may be obtained from the remaining either by imposition of parameter restrictions or through a limiting process. Although our primary focus is on nonnested hypothesis testing, we also briefly discuss the problem of model selection and discuss the differences and similarities of the two approaches. By utilizing the linear regression model as a convenient framework, we examine three broad approaches to nonnested hypothesis testing: the modified (centred) loglikelihood ratio procedure, the comprehensive models approach; and the encompassing procedure. Finally, we consider a number of practical problems which arise in the application of nonnested tests to nonlinear models such as the probit and logit qualitative response models.
JEL Classifications: C1, C20, C52
Keywords: Nonnested hypothesis, model selection, Cox test, ecompassing, stochastic simulation, KullbackLeibler divergence measure.
Web Link: http://www.blackwellpublishing.com/book.asp?ref=9781405106764

“BOUNDS TESTING APPROACHES TO THE ANALYSIS OF LEVEL RELATIONSHIPS” M Hashem Pesaran, Yongcheol Shin and Richard J Smith. Journal of Applied Econometrics special issue in honour of J D Sargan on the theme “Studies in Empirical Macroeconometrics”, (eds) D.F. Hendry and M.H. Pesaran, 2001, Vol.16 pp.289326.
Abstract: This paper develops a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend or firstdifference stationary. The proposed tests are based on standard F and t statistics used to test the significance of the lagged levels of the variables in a univariate error correction mechanism. The asymptotic distributions of these statistics are nonstandard under the null hypothesis that there exists no level relationship, irrespective of whether the regressors are I(0) or I(1). Two sets of asymptotic critical values are provided: one when all the regressors are I(1) and the other if they are all purely I(0). These two sets of critical values provide a band covering all possible classifications of the regressors into purely I(0), purely I(1)or mutually cointegrated. Accordingly, various bounds testing procedures are proposed. It is shown that the proposed tests are consistent, and their asymptotic distribution under the null and suitably defined local alternatives are derived. The empirical relevance of the bounds procedures is demonstrated by a reexamination of the earnings equation included in the UK Treasury macroeconometric model. This is a particularly relevant application as there is considerable doubt concerning the order of integration of variables such as the unemployment rate, the union strength and the wedge between the “real product wage” and the “real consumption wage” that enter the earnings equation.
JEL Classification: C12, C22, C32, E24.
Key Words: Level Relationship, Unrestricted Error Correction Model, Cointegration, Unit Roots, Bounds Tests, Critical Value Bounds, Asymptotic Local Power, Earnings Equation.
Full text: http://www3.interscience.wiley.com/cgibin/jissue/84502477?CRETRY=1&SRETRY=0

"LIFECYCLE CONSUMPTION UNDER SOCIAL INTERACTIONS", Michael Binder and M. Hashem Pesaran. Journal of Economic Dynamics and Control, special issue on Computational Methods in Economic Dynamics and Finance, (ed) Sean Holly, 2001, Vol.25 pp.3583.
Abstract: In this paper we examine how social interactions affect consumption decisions at various levels of aggregation in a lifecycle economy made up of peer groups. For this purpose, we consider two analytically solvable lifecycle models, one under certainty equivalent behavior and one under prudence, and explicitly allow for three different forms of social interactions in peer groups, namely conformism, altruism, and jealousy. We show that whether social interactions have any effects on individuals’ optimal consumption decisions critically depends on intertemporal rather than static considerations. This is true regardless of whether individuals’ preferences are time separable or exhibit habit formation, and whether information within peer groups is homogeneous or disparate. It implies that analyzing the effects of social interactions in static rather than intertemporal settings is likely to be misleading. We also show that social interactions, when coupled with either habit formation or prudence, can significantly strengthen the effects of habit formation or prudence in the direction of resolving two wellknown puzzles in the literature on the permanent income hypothesis, namely excess smoothness and excess sensitivity.
JELClassification: D91, E21
Keywords: LifeCycle Model, Social Interactions, Habit Formation, Disparate Information, Prudence.
Full text: http://dx.doi.org/10.1016/S01651889(99)00069X

“ECONOMIC TRENDS AND MACROECONOMIC POLICIES IN POSTREVOLUTIONARY IRAN” M. Hashem Pesaran. In (ed) Parvin Alizadeh, The Economy of Iran: Dilemmas of an Islamic State, (2000) London: I.B. Tauris, chapter 2, pp.63100. ISBN 1860644643
Abstract: This paper reviews some of the main trends in the Iranian economy over the past two decades and discusses the key economic policy issues that divide the reformist from the more conservative factions in Iran. It argues that the economic policy dilemma of whether to liberalize the economy has not gone away and very much lies dormant. For a small open economy such as Iran operating in an increasingly globalized world economic environment, the neglect of fundamental economic forces in favour of political vested interest can have dire consequences in the long run.
JEL Classifications: E60, E66, O21.
Key words: Macroeconomic Trends, Monetary and Exchange Rate Policies, Islamic Republic of Iran.
Full text: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/iran98_0.pdf

“ECONOMIC AND STATISTICAL MEASURES OF FORECAST ACCURACY” Clive W.J. Granger and M Hashem Pesaran. Journal of Forecasting, 2000, Vol. 19, pp.537560.
Abstract: This paper argues in favour of a closer link between the decision and the forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used with meteorological forecasts, it is hardly mentioned in standard academic texts on economic forecasting. Some of the main issues involved are illustrated in the context of a twostate, twoaction decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and useful links between Kuipers score used as a measure of forecast accuracy in the meteorology literature and the market timing tests used in finance are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be exploited in the presence of transaction costs are discussed.
JEL Classifications: C10, C20, C22, C52
Key Words: Decision theory, forecast evaluation, probabilistic forecasts, economic and statistical measures of forecast accuracy, stock market predictability.
Full Text: http://www3.interscience.wiley.com/cgibin/jissue/76502342

“A STRUCTURAL COINTEGRATING VAR APPROACH TO MACROECONOMETRIC MODELLING" Anthony Garratt, Kevin Lee, M. Hashem Pesaran and Yongcheol Shin. In (eds) Sean Holly and Martin Weale, Econometric Modelling: Techniques and Applications, (2000) Cambridge: Cambridge University Press, chapter 5, pp.94131. ISBN 0521650690
Abstract: In this paper we discuss the 'structural cointegrating VAR' approach to macroeconometric modelling and compare it to other approaches currently followed in the literature, namely the largescale simultaneous equation macroeconometric models, the structural VARs, and the dynamic stochastic general equilibrium models. The structural cointegrating VAR approach has the attractive features that the estimated longrun relationships embedded in the model are theory consistent, and have a clear economic interpretation, and yet the shortrun dynamics are flexibly estimated within a VAR framework. The approach is illustrated using a small quarterly macroeconometric model of the UK. The uses of the model in impulse response analysis and probability forecasting is also discussed.
JEL Classifications: C5, C32, E17
Keywords: Structural Cointegrating VAR, Macroeconomic Modelling, Generalised Impulse Responses, Persistence Profiles, Probability Forecasts.
Web link: http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=0521650690

“STRUCTURAL ANALYSIS OF VECTOR ERROR CORRECTION MODELS WITH EXOGENOUS I(1) VARIABLES" M Hashem Pesaran, Yongcheol Shin and Richard J Smith. Journal of Econometrics, 2000, Vol 97, pp.293343.
Abstract: This paper generalizes the existing cointegration analysis literature in two respects. Firstly, the problem of efficient estimation of vector error correction models containing exogenous I(1) variables is examined. The asymptotic distributions of the (log) likelihood ratio statistics for testing cointegrating rank are derived under different intercept and trend specifications and their respective critical values are tabulated. Tests for the presence of an intercept or linear trend in the cointegrating relations are also developed together with model misspecification tests. Secondly, efficient estimation of vector error correction models when the shortrun dynamics may differ within and between equations is considered. A reexamination of the purchasing power parity and the uncovered interest rate parity hypotheses is conducted using UK data under the maintained assumption of exogenously given foreign and oil prices.
JEL Classifications: C12, C13, C32
Keywords: Structural Vector Error Correction Model, Cointegration, Unit Roots, Likelihood Ratio Statistics, Critical Values, Seemingly Unrelated Regression, Monte Carlo Simulations, Purchasing Power Parity, Uncovered Interest Rate Parity.
Full text: http://dx.doi.org/10.1016/S03044076(99)000731

“A DECISION THEORETIC APPROACH TO FORECAST EVALUATION” C.W.J. Granger and M. Hashem Pesaran. In (eds) W.S. Chan, W.K. Li and Howell Tong, Statistics and Finance: An Interface, Imperial College Press, London, 2000, chapter 15, pp.261278. ISBN 1860942377.
Abstract: This paper addresses the problem of forecast evaluation in the context of a simple but realistic decision problem, and proposes a procedure, for the evaluation of forecasts based on their average realized value to the decision maker. It is shown that by concentrating on probability forecasts stronger theoretical results can be achieved than if just event forecasts were used. A possible generalisation is considered concerning the use of the correct, conditional predictive density function when forming forecasts.
JEL Classifications: C10, C20, C22
Keywords: Forecast evaluation, cost function, probabilistic forecasts.
Web Link: http://www.icpress.co.uk/books/mathematics/p202.html

“NEGLECTED HETEROGENEITY AND DYNAMICS IN CROSSCOUNTRY SAVINGS REGRESSIONS” M. Hashem Pesaran, Nadeem U. Haque and Sunil Sharma. In (eds) J. Krishnakumar and E. Ronchetti, Panel Data Econometrics  Future Direction: Papers in Honour of Professor Pietro Balestra. In the series, “Contributions to Economic Analysis”, Elsevier Science, 2000, chapter 3, pp.5382. ISBN 0444502378.
Abstract: This paper examines the extent to which conclusions of crosscountry studies of private savings are robust to allowing for the possible heterogeneity of saving behaviour across countries and the inclusion of dynamics. It provides a review of the econometric implications of neglected slope heterogeneity and dynamics for the fixed effects estimators routinely used in such studies, and illustrates the nature and extent of the biases involved by a reexamination of time series data from 21 OECD countries previously analysed in the literature. The paper shows that neglecting heterogeneity and dynamics in crosscountry savings regressions can lead to misleading inferences about the key determinants of savings behavior. If differences across countries are ignored, one can overestimate the influence of certain factors on the private savings rates and at the same time obtain highly significant, but spurious, nonlinear effects for some of the potential determinants. The results indicate that among the many variables considered in the literature only the fiscal variables  the general government surplus as a proportion of GDP and the ratio of government consumption to GDP seem to be the key determinants of private savings rates in the industrial countries in the post world war II period.
JEL Classifications: E21, C23
Key Words: Saving Behaviour, Crosscountry Studies, Slope Heterogeneity, Dynamics, Panel Data Models
Web Links: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/pp10/2000(Ch3)Neglectedheterogeneitydynamics(withHaqueSharma).pdf
Tables: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/savtab.pdf

“SOLUTION OF NONLINEAR RATIONAL EXPECTATIONS MODELS WITH APPLICATIONS TO FINITEHORIZON LIFECYCLE MODELS OF CONSUMPTION” Michael Binder, M Hashem Pesaran and S. Hossein Samiei. Journal of Computational Economics, 2000, Vol 15, pp.2557.
Abstract: This paper considers the solution of nonlinear rational expectations models resulting from the optimality conditions of a finitehorizon intertemporal optimization problem satisfying Bellman's principle of optimality (and possibly involving inequality constraints). A backward recursive procedure is used to characterize and solve the timevarying optimal decision rules generally associated with these models. At each stage of these backward recursions, either an analytical or numerical solution of the optimality conditions is required. When an analytical solution is not possible, a minimum weighted residual approach is used. The solution technique is illustrated using a lifecycle model of consumption under labor income and interest rate uncertainties (and possibly involving liquidity constraints). Approximate numerical solutions are provided and compared with certaintyequivalent solutions and, when possible, with exact solutions.
JELClassification: C60, D90, E20.
Keywords: Nonlinear Rational Expectations Models, Intertemporal Consumer Choice, Minimum Weighted Residual Method, Exact and CertaintyEquivalent Solutions.
Full Text: http://dx.doi.org/10.1023/A:1008634709790

"THE LIFE AND WORK OF JOHN RICHARD NICHOLAS STONE 19131991" M Hashem Pesaran and Geoff Harcourt, Economic Journal, 2000, Vol 110, pp.F146F165.
Abstract: Sir Richard Stone, knighted in 1978 and Nobel Laureate in Economics in 1984, was one of the pioneering architects of national income and social accounts, and one of the few economists of his generation to have faced the challenge of economics as a science by combining theory and measurement within a cohesive framework. He was awarded the Nobel Prize in Economics for his “fundamental contributions to the development of national accounts”, but made equally significant contributions to the empirical analysis of consumer behaviour. His work on the “Growth Project” was also instrumental in the development of appropriate econometric methodology for the construction and the analysis of large disaggregated macroeconometric models. This paper provides an analysis of Stone’s many contributions.
JEL Classification: B3, C8, E2 O4
Key Words: National Income Accounting; Consumer Behaviour; Macroeconometric Modelling.
Web Link: http://www.res.org.uk/journals/abstracts.asp?ref=00130133&vid=110&iid=461&aid=511

“CROSSSECTIONAL AGGREGATION OF NONLINEAR MODELS” Kees Jan van Garderen, Kevin Lee and M. Hashem Pesaran. Journal of Econometrics, 2000, Vol 95, pp.285331.
Abstract: This paper considers the problem of crosssectional aggregation when the underlying micro behavioural relations are characterized by general nonlinear specifications. It focuses on forecasting the aggregates, and shows how an optimal aggregate model can be derived by minimizing the mean squared prediction errors conditional on the aggregate information. The paper also derives model selection criteria for distinguishing between aggregate and disaggregate models when the primary object of the analysis is forecasting the aggregates, and establishes the consistency of the model selection criteria in large samples. In the case of standard nonlinear micro relations with additive errors it also provides suitable small sample corrections. For more general nonlinear specifications we consider bootstrap techniques to correct for small sample bias of the proposed model selection criteria. Some of the ideas in the paper are illustrated using loglinear micro relations, often employed in applied research. The paper also contains an empirical application where loglinear production functions are estimated for the UK economy disaggregated by eight industrial sectors and at the aggregate level over the period 19541995.
JEL Classifications: C43, C52, E23
Keywords: Aggregation, Prediction, Model selection, Nonlinear models , Loglinear specifications, Production functions, Parametric bootstrap.
Full Text: http://dx.doi.org/10.1016/S03044076(99)000408
Tables: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/aggtab.pdf

“RECURSIVE MODELLING APPROACH TO PREDICTING UK STOCK RETURNS” M Hashem Pesaran and Allan Timmermann. Economic Journal, 2000, Vol 110, pp.159191.
Abstract: This paper applies an extended and generalized version of the recursive modelling strategy developed in Pesaran and Timmermann (1995) to the UK stock market. The focus of the analysis is to simulate investors' search in ‘real time’ for a model that can forecast stock returns. It demonstrates the extent to which monthly stock returns in the UK were predictable over the period 19701993. Due to a set of unique historical circumstances, UK stock returns were extremely volatile in 19741975, and we discuss how to design a modelling approach capable of accounting for this and similar low probability events. We find evidence of both longterm and shortterm predictability in UK stock returns, which could have been exploited by investors to improve on the riskreturn tradeoff offered by a passive strategy in the market portfolio. Alternative interpretations of this finding are briefly discussed.
JEL Classifications: G11, G12, E17, E44
Keywords: Stock returns, UK stock market, recursive modeling, switching portfolio, predicting stock prices.
Web Link: http://www.res.org.uk/journals/abstracts.asp?ref=00130133&vid=110&iid=460&aid=495
Tables: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/uktab.pdf

"SOLUTION OF FINITEHORIZON MULTIVARIATE LINEAR RATIONAL EXPECTATIONS MODELS AND SPARSE LINEAR SYSTEMS" Michael Binder and M. Hashem Pesaran. Journal of Economic Dynamics and Control, 2000, Vol 24 pp.325346.
Abstract: This paper presents efficient methods for the solution of finitehorizon multivariate linear rational expectations models, linking the solution of such models to the problem of solving sparse linear equation systems with a blocktridiagonal coefficient matrix structure. Two numerical schemes for the solution of sparse linear equation systems with a blocktridiagonal coefficient matrix structure are discussed, and it is shown how these procedures can be readily adapted to efficiently solve finitehorizon multivariate linear rational expectations models. As the two numerical schemes are fully recursive and only involve elementary matrix operations, they are also straightforward to implement. The numerical schemes are illustrated by applying them to a general finitehorizon adjustment cost problem of expenditure shares, and to a finitehorizon linearquadratic optimal control problem.
JELClassification: C32, C63.
Keywords: Multivariate Linear Rational Expectations Models, Sparse Linear Systems.
Full text (Europe): http://dx.doi.org/10.1016/S01651889(99)000081
Full text (USA): http://www.inform.umd.edu/ENGR/.FacultyStaff/mbinder/research/resparse.html

"AN AUTOREGRESSIVE DISTRIBUTED LAG MODELLING APPROACH TO COINTEGRATION ANALYSIS" M Hashem Pesaran and Yongcheol Shin, in (ed) S Strom, Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, 1999, chapter 11. Cambridge University Press, Cambridge. ISBN 0521633230 (hb) 0521 633656 (pb)
Abstract: This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of longrun relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the shortrun parameters are pT consistent with the asymptotically singular covariance matrix, and the ARDLbased estimators of the longrun coefficients are superconsistent, and valid inferences on the longrun parameters can be made using standard normal asymptotic theory. The paper also examines the relationship between the ARDL procedure and the fully modified OLS approach of Phillips and Hansen to estimation of cointegrating relations, and compares the small sample performance of these two approaches via Monte Carlo experiments. These results provide strong evidence in favour of a rehabilitation of the traditional ARDL approach to time series econometric modelling. The ARDL approach has the additional advantage of yielding consistent estimates of the longrun coefficients that are asymptotically normal irrespective of whether the underlying regressors are I(1) or I(0).
JEL Classifications: C12, C13, C15, C22.
Key Words: Autoregressive distributed lag model, Cointegration, I(1) and I(0) regressors, Model selection, Monte Carlo simulation.
Web Link: http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=0521633230
Tables: http://www.econ.cam.ac.uk/peoplefiles/emeritus/mhp1/ardltab.pdf

"STOCHASTIC GROWTH MODELS AND THEIR ECONOMETRIC IMPLICATIONS" Michael Binder and M Hashem Pesaran. Journal of Economic Growth, 1999, Vol 4 pp.139183.
Abstract: This paper considers the consequences of explicitly allowing for stochastic technological progress and stochastic labor input in the discrete time SolowSwan and ‘AK’ growth models. It shows that the capitaloutput ratio, but not output per capita, is ergodic irrespective of whether there is a unit root in technology, and thus is the more appropriate measure to use in the crosssectional analysis of the growth process. Furthermore, the paper derives the crosssectional and timeseries implications of the stochastic SolowSwan model and contrasts these to those of its deterministic counterpart. Among these implications are that the mean of the capitaloutput ratio depends in a precise way not only on the saving rate and the growth rate of labor input, but also on the variance and higherorder cumulants of the capitaloutput ratio. Using the SummersHeston data for 72 countries from 1960 to 1992, strong support is found for the predictions of the stochastic SolowSwan model as compared to those of its deterministic counterpart (as well as those of the ‘AK’ model), including a significant negative crosssectional relationship between the mean and the variance of the capitaloutput ratio.
Keywords: Stochastic Growth Models, Ergodicity, Econometric Implications, CrossCountry Growth Regressions.
JELClassifications: C10, E20, O40.
Full text: http://dx.doi.org/10.1023/A:1009802421114

"POOLED MEAN GROUP ESTIMATION OF DYNAMIC HETEROGENEOUS PANELS" M Hashem Pesaran, Yongcheol Shin and Ron Smith. Journal of the American Statistical Association, 1999, Vol 94 pp.621634.
Abstract: It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the Mean Group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this paper, we propose an intermediate procedure, referred to as the Pooled Mean Group (PMG) estimator, which constrains the long run coefficients to be identical, but allows the short run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: aggregate consumption functions for 24 OECD economies over the period 196293, and energy demand functions for 10 Asian developing economies over the period 197490.
JELClassification: C13 C23, C63, E21.
Keywords: Heterogeneous dynamic panels, pooled mean group estimator, I(0) and I(1) regressors, consumption functions, energy demand.
Web Link: http://www.tandfonline.com/doi/abs/10.1080/01621459.1999.10474156
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“BAYES ESTIMATION OF SHORTRUN COEFFICIENTS IN DYNAMIC PANEL DATA MODELS” Cheng Hsiao, M Hashem Pesaran and A. Kamil Tahmiscioglu, in C. Hsiao, K. Lahiri, LF Lee and M.H. Pesaran (eds), Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G S Maddala Cambridge University Press, Cambridge, 1999, chapter 11, pp.268296. ISBN 0 521 63169 6.
Abstract: This study is concerned with estimating the mean of the coefficients in a dynamic panel data model when the coefficients are assumed to be randomly distributed across crosssectional units. We suggest a Bayes approach to the estimation of such models using Markov chain Monte Carlo methods. We establish the asymptotic equivalence of the Bayes estimator and the mean group estimator proposed by Pesaran and Smith (1995), and show that the Bayes estimator is asymptotically normal for large N (the number of units) and large T (the number of time periods) so long as root N/T > 0 as both N and T > infinity. The performance of the Bayes estimator for the shortrun coefficients in dynamic panels is also compared against alternative estimators using both simulated and real data. The Monte Carlo results show that the Bayes estimator has better sampling properties than other estimators for both small and moderate T samples. The analysis of Tobin's q model yields new results.
Full Text: http://www.cambridge.org/catalogue/catalogue.asp?isbn=9780521631693&ss=toc

With Z. Zhao, Bias Reduction in Estimating Longrun Relationships from Dynamic Heterogeneous Panels in C. Hsiao, K. Lahiri, LF Lee and M.H. Pesaran (eds), Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G S Maddala Cambridge University Press, Cambridge, 1999, chapter 12, pp.297321. ISBN 0 521 63169 6.

With L.W. Taylor, Diagnostics for IV Regressions Oxford Bulletin of Economics and Statistics, 1999, Vol.61 pp.255281.

With F.J. RugeMurcia, Analysis of Exchange Rate Target Zones using a LimitedDependent Rational Expectations Model with Jumps (Program and Data) Journal of Business and Economic Statistics, 1999, Vol.17 pp.5066.
Abstract: This paper examines the exchange rate determination in target zone regime using a LimitedDependent Rational Expectations (LDRE) model where the bounds can be fixed for an extended period, but are subject to occasional jumps. In this case, the behavior of the endogenous variable is affected by the agents’ expectations about both the occurrence and the size of the jump. The RE solution is derived and shown to encompass the cases of perfectly predictable and stochastically varying bounds examined by earlier literature. We demonstrate that the solution exists for all the parameter values and is unique if the coefficient of the expectational variable is less than or equal to one. These results hold even when the jump probability is stochastically varying and the error terms are conditionally heteroskedastic. The model is estimated using data for the Franc/Mark exchange rate. Empirical results provide support for the nonlinear model with timevarying realignment probability and indicate that the agents correctly anticipated most of the observed changes in the central parity.
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