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  • "Estimation of Time-invariant Effects in Static Panel Data Models", by M. Hashem Pesaran and Qiankun Zhou, forthcoming in Econometrics Reviews, June 2016.

    Abstract: This paper proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. The FEF-IV allows for endogenous time-invariant regressors but assumes that there exists a suficient number of instruments for such regressors. It is shown that the FEF and FEF-IV 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 FEF-IV estimators which are shown to be consistent under fairly general conditions. The small sample properties of the FEF and FEF-IV 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 time-invariant regressors are endogenous, the FEF-IV 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 sub-set of time-varying 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, time-invariant effects, endogenous time-invariant regressors, Monte Carlo experiments, fixed effects filtered estimators.
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    Supplementary Data:
    Stata Code and Instructions:

  • "A multi-country approach to forecasting output growth using PMIs", by Alexander Chudik, Valerie Grossmanz and M. Hashem Pesaran, forthcoming in the Journal of Econometrics, January 2016.

    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/Tk for some 0 < k < ∞) to the infeasible optimal forecasts obtained from a factor-augmented high-dimensional 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 data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based 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 data-rich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.
    JEL Classifications: C53, E37.
    Key Words: Global VARs, High-dimensional VARs, Augmented GVAR, Forecasting, Nowcasting, Data-rich methods, GDP and PMIs
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  • "Is There a Debt-threshold Effect on Output Growth?", by Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran, and Mehdi Raissi, forthcoming in the Review of Economics and Statistics, November 2015. Abstract: This paper studies the long-run impact of public debt expansion on economic growth and investigates whether the debt-growth 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 cross-sectionally 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 1965-2010 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 long-run effects of public debt build-up 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, long-run relationships, estimation and inference, large dynamic heterogeneous panels, cross-section dependence, debt, and inflation.
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    Matlab Codes for the CS-DL Estimators:
    Matlab Codes for Panel Tests of Threshold Effects:

  • "Exponent of Cross-sectional Dependence: Estimation and Inference", by Natalia Bailey, George Kapetanios and M. Hashem Pesaran, forthcoming in the Journal of Applied Econometrics, January 2015.

    Abstract: In this paper, we provide a characterisation of the degree of cross-sectional dependence in a two dimensional array, [code] in terms of the rate at which the variance of the cross-sectional 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 cross-sectional dependence by , defined by the standard deviation, [code], where [code] is a simple cross-sectional 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 inter-linkages 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, Cross-sectional dependence, Cross-sectional averages, Weak and strong factor models
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    Supplementary Appendices:
    Codes and Data:

  • "A Two Stage Approach to Spatio-Temporal Analysis with Strong and Weak Cross-Sectional Dependence", by Natalia Bailey, Sean Holly, and M. HashemPesaran, CESifo Working Paper No. 4592, forthcoming in the Journal of Applied Econometrics, January 2015.

    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 cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored 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 spatio-temporal model for the de-factored 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, spatio-temporal models, positive and negative connections, house price changes.
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