Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variables and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be...

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Vydáno v:Journal of econometrics Ročník 188; číslo 2; s. 393 - 420
Hlavní autoři: Chudik, Alexander, Pesaran, M. Hashem
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.10.2015
Elsevier Sequoia S.A
Témata:
Asymptotic methods
Bias
Coefficient heterogeneity
Common correlated effects
Cross section dependence
Data models
econometric models
Econometrics
economic analysis
economic theory
equations
Estimation and inference
Estimation bias
Experiments
Heterogeneity
Lagged dependent variable
Large panels
Monte Carlo method
Monte Carlo simulation
Panel data
Recursion
Studies
Time series
time series analysis
Unobserved common factors
Variables
C31
C33
Unobserved common factors
Cross section dependence
Lagged dependent variable
Estimation and inference
Common correlated effects
Large panels
Coefficient heterogeneity
ISSN:0304-4076, 1872-6895
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Shrnutí:This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variables 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 de-meaning 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.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2015.03.007