This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension () and the cross-section dimension () are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL 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 CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when is not too large and lies in the range of 30–50.
We are grateful to Ron Smith and participants at the Conference in Honor of Aman Ullah held on 13–14 March 2015 at UC Riverside for constructive comments and suggestions. We would also like to thank the editors and two anonymous referees for helpful suggestions. The views expressed in this paper are those of the authors and do not necessarily represent those of Federal Reserve Bank of Dallas, the Federal Reserve System, the International Monetary Fund or IMF policy. Hashem Pesaran acknowledges financial support under ESRC Grant No. ES/I031626/1.
Chudik, A., Mohaddes, K., Pesaran, M.H. and Raissi, M. (2016), "Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors", Essays in Honor of man Ullah (Advances in Econometrics, Vol. 36), Emerald Group Publishing Limited, Leeds, pp. 85-135. https://doi.org/10.1108/S0731-905320160000036013
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