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Working Papers

 

Archives: WP version of published papers

 

  • "Estimation and inference for spatial models with heterogeneous coefficients: an application to U.S. house prices", by Michele Aquaro, Natalia Bailey and M. Hashem Pesaran, CESifo WP Series No. 7542, March 2019. This paper was previously titled “Quasi-maximum likelihood estimation of spatial models with heterogeneous coefficient” (CESifo WP Series No. 5428)

    Abstract: This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered.
    JEL Classifications: C210, C230
    Key Words: spatial panel data models, heterogeneous spatial lag coefficients, identification, quasi maximum likelihood (QML) estimators, non-Gaussian errors, house price changes, Metropolitan Statistical Areas.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp19/Aquaro_Bailey_Pesaran_Estimation_and_inference_for_spatial_models_with_cesifo_wp7542.pdf

     

  • "Estimation and Inference in Spatial Models with Dominant Units", by M. Hashem Pesaran and Cynthia Fan Yang, March 2019

    Abstract: Estimation and inference in the spatial econometrics literature are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of cross-section units, n. In this paper, we consider spatial models where this restriction is relaxed. The linear-quadratic central limit theorem of Kelejian and Prucha (2001) is generalized and then used to establish the asymptotic properties of the GMM estimator due to Lee (2007) in the presence of dominant units. A new Bias-Corrected Method of Moments estimator is also proposed that avoids the problem of weak instruments by self-instrumenting the spatially lagged dependent variable. Both estimators are shown to be consistent and asymptotically normal, depending on the rate at which the maximum column sum of the weights matrix rises with n. The small sample properties of the estimators are investigated by Monte Carlo experiments and shown to be satisfactory. An empirical application to sectoral price changes in the US over the pre- and post-2008 …nancial crisis is also provided. It is shown that the share of capital can be estimated reasonably well from the degree of sectoral interdependence using the input-output tables, despite the evidence of dominant sectors being present in the US economy.
    JEL Classifications: C13, C21, C23, R15.
    Key Words: spatial autoregressive models, central limit theorems for linear-quadratic forms, dominant units, GMM, bias-corrected method of moments (BMM), US input- output analysis, capital share.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp19/PY_SAR_BMM_Main_and_Appendix_March_10_2019.pdf
     

  • "Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR", by Alexander Chudik, M. Hashem Pesaran and Kamiar Mohaddes, December 2018

    Abstract: The paper contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one third of the long-horizon forecast error variance of output growth, and about one fifth of the long run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross country average of the median debt elasticity is 1.58 when the rise in output is due to a fiscal expansion as compared to 0.75 when the rise in output follows from a favorable output shock.
    JEL Classifications: C30, E62, H6.
    Key Words: Factor-augmented VARs, Global VARs, identi…cation of global and country-specific shocks, Bayesian analysis, public debt and output growth, debt elasticity.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp18/CMP_GVAR_debt_2018_12_26_rs.pdf
     

  • "Detection of Units with Pervasive Effects in Large Panel Data Models", by George Kapetanios, M. Hashem Pesaran and Simon Reese, CESifo Working Papers No. 7401, November 2018, revised April 2019

    Abstract: The importance of units with pervasive impacts on a large number of other units in a network has become increasingly recognized in the literature. In this paper we propose a new method to detect such pervasive units by basing our analysis on unit-specific residual error variances in the context of a standard factor model, subject to suitable adjustments due to multiple testing. Our proposed method allows us to estimate and identify pervasive units having neither a priori knowledge of the interconnections amongst cross-section units nor a short list of candidate units. It is applicable even if the cross section dimension exceeds the time dimension, and most importantly it could end up with none of the units selected as pervasive when this is in fact the case. The sequential multiple testing procedure proposed exhibits satisfactory small-sample performance in Monte Carlo simulations and compares well relative to existing approaches. We apply the proposed detection method to sectoral indices of US industrial production, US house price changes by states, and the rates of change of real GDP and real equity prices across the world's largest economies.
    JEL Classifications: C18, C23, C55.
    Key Words: Pervasive units, factor models, systemic risk, cross-sectional dependence.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp19/KPR_dominantunits_23_April_2019.pdf
     

  • "Short T Dynamic Panel Data Models with Individual and Interactive Time Effects", by Kazuhiko Hayakawa, M. Hashem Pesaran and L. Vanessa Smith, September 2018

    Abstract: This paper proposes a quasi maximum likelihood estimator for short T dynamic fixed effects panel data models allowing for interactive time effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, whilst at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. Order conditions for identification of the number of interactive effects are established, and conditions are derived under which the parameters are almost surely locally identified. It is shown that global identification is possible only when the model does not contain lagged dependent variables. The QML estimator is proven to be consistent and asymptotically normally distributed. A sequential multiple testing likelihood ratio procedure is also proposed for estimation of the number of factors which is shown to be consistent. Finite sample results obtained from Monte Carlo simulations show that the proposed procedure for determining the number of factors performs very well and the quasi ML estimator has small bias and RMSE, and correct empirical size even when the number of factors is estimated. An empirical application, revisiting the growth convergence literature is also provided.
    JEL Classifications: C12, C13, C23.
    Key Words: short T dynamic panels, unobserved common factors, quasi maximum likelihood, interactive time effects, multiple testing, sequential likelihood ratio tests, output and growth convergence.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp18/HPS_2_September_2018.pdf

     

  • "Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models", by Matthew Harding, Carlos Lamarche and M. Hashem Pesaran. August 2018

    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 by Pesaran (2006) and Chudik and Pesaran (2015) 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. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. 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://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp18/LamarcheHardingPesaran_Dynamic_Panel_Quantile_Paper_and_Supplement_August_2018.pdf
     

  • "Land Use Regulations, Migration and Rising House Price Dispersion in the U.S.", by Wukuang Cun and M. Hashem Pesaran. April 2018, revised October 2018

    Abstract: This paper develops and solves a dynamic spatial equilibrium model of regional housing markets in which house prices are jointly determined with location-to-location migration flows. Agents optimize period-by-period and decide whether to remain where they are or migrate to a new location at the start of each period. The agent'’s optimal location choice and the resultant migration process is shown to be Markovian with the transition probabilities across all location pairs given as non-linear functions of wage and housing cost differentials, which are time varying and endogenously determined. On the supply side, in each location the construction …firms build new houses by combining land and residential structures; with housing supplies endogenously responding to migration flows. The model can be viewed as an example of a dynamic network where regional housing markets interact with each other via migration flows that function as a source of spatial spill-overs. It is shown that the deterministic version of the model has a unique equilibrium and a unique balanced growth path. We estimate the state-level supplies of new residential land from the model using housing market and urban land acreage data. These estimates are shown to be significantly negatively correlated with the Wharton Residential Land Use Regulatory Index. The model can simultaneously account for the rise in house price dispersion and the interstate migration in the U.S.. Counterfactual simulations suggest that reducing either land supply differentials or migration costs could significantly lower house price dispersion.
    JEL Classifications: E0, R23, R31
    Key Words: House price dispersion, endogenous location choice, interstate migration, land-use restriction, spatial equilibrium.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp18/PC_Housing_Paper_2018_10_09.pdf
    SSRN Working Paper Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3162399

  • "A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels", by Alexander Chudik and M. Hashem Pesaran, CESifo WP no. 6688. October 2017

    Abstract: This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the proposed approach lies in the fact that, by construction, the instruments have maximum correlation with the target variables and the problem of weak instrument is thus avoided. The proposed approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. In this paper we focus on the latter and consider both univariate and multivariate panel data models with short time dimension. Simple Bias-corrected Methods of Moments (BMM) estimators are proposed and shown to be consistent and asymptotically normal, under very general conditions on the initialization of the processes, individual-speci…c effects, and error variances allowing for heteroscedasticity over time as well as cross-sectionally. Monte Carlo evidence document BMM’s good small sample performance across different experimental designs and sample sizes, including in the case of experiments where the system GMM estimators are inconsistent. We also …nd that the proposed estimator does not suffer size distortions and has satisfactory power performance as compared to other estimators.
    JEL Classifications: C12, C13, C23.
    Key Words: Short-T Dynamic Panels, GMM, Weak Instrument Problem, Quadratic Moment Conditions, Panel VARs, Monte Carlo Evidence.
    Full Text: http://www.econ.cam.ac.uk/emeritus/mhp1/wp17/CP_BMM_2017_Sept20wp.pdf
     

  • "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities", by M. Hashem Pesaran and Takashi Yamagata, March 2017, revised January 2018

    Abstract: This paper considers tests of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. We focus on class of tests that are based on Student t tests of individual securities which have a number of advantages over the existing standardised Wald type tests, and propose a test procedure that allows for non-Gaussianity and general forms of weakly cross correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5; 000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically signifi…cant evidence against Sharpe-Lintner CAPM and Fama-French three factor models are found mainly during the recent fi…nancial crisis. Also we fi…nd a signifi…cant negative correlation between a twelve-months moving average p-values of the test and excess returns of long/short equity strategies (relative to the return on S&P 500) over the period November 1994 to June 2015, suggesting that abnormal pro…ts are earned during episodes of market ineffiencies.
    JEL Classifications: C12, C15, C23, G11, G12
    Key Words: CAPM, Testing for alpha, Weak and spatial error cross-sectional dependence, S&P 500 securities, Long/short equity strategy.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp18/PY_LFPM_30_Jan_2018.pdf
    Supplement: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp17/PY_LFPM_11_March_2017_Supplement.pdf
     

  • "Business Cycle Effects of Credit Shocks in a DSGE Model with Firm Defaults", by M. Hashem Pesaran and TengTeng Xu, CWPE Working paper. No. 1159, CESifo Working Paper No. 3609, IZA Discussion Paper No. 6027, October 2011, revised April 2016

    Abstract: This paper proposes a new theoretical framework for the analysis of the relationship between credit shocks, firm defaults and volatility. The key feature of the modelling approach is to allow for the possibility of default in equilibrium. The model is then used to study the impact of credit shocks on business cycle dynamics. It is assumed that firms are identical ex ante but differ ex post due to different realizations of firm-specific technology shocks, possibly leading to default by some firms. The implications of firm defaults for the balance sheets of households and banks and their subsequent impacts on business uctuations are investigated within a dynamic stochastic general equilibrium framework. Results from a calibrated version of the model suggest that, in the steady state, a firm's default probability rises with its leverage ratio and the level of uncertainty in the economy. A positive credit shock, defined as a rise in the loan-to-deposit ratio, increases output, consumption, hours and productivity, and reduces the spread between loan and deposit rates. Interestingly, the effects of the credit shock tend to be highly persistent, even without price rigidities and habit persistence in consumption behavior.
    JEL Classifications: E32, E44, E50, G21.
    Key Words: Firm Defaults; Credit Shocks; Financial Intermediation; Interest Rate Spread; Uncertainty.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp16/MacroCredit_PesaranXu_April-2016.pdf
    Supplement: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp11/MacroCredit_ 5Oct2011_Supplement.pdf

     

  • "Optimality and Diversifiability of Mean Variance and Arbitrage Pricing Portfolios", by M. Hashem Pesaran, and Paolo Zaffaroni, CESifo Working Papers No. 2857, October, 2009

    Abstract: This paper investigates the limit properties of mean-variance (mv) and arbitrage pricing (ap) trading strategies using a general dynamic factor model, as the number of assets diverge to infinity. It extends the results obtained in the literature for the exact pricing case to two other cases of asymptotic no-arbitrage and the unconstrained pricing scenarios. The paper characterizes the asymptotic behaviour of the portfolio weights and establishes that in the non-exact pricing cases the ap and mv portfolio weights are asymptotically equivalent and, moreover, functionally independent of the factors conditional moments. By implication, the paper sheds light on a number of issues of interest such as the prevalence of short-selling, the number of dominant factors and the granularity property of the portfolio weight.
    JEL Classifications: Large Portfolios, Factor Models, Mean-Variance Portfolio, Arbitrage Pricing, Market (Beta) Neutrality, Well Diversification.
    Key Words: C32, C52, C53, G11.
    Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/wp09/pz_port_17_October_09.pdf