Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/74513
Authors: 
Chudik, Alexander
Pesaran, M. Hashem
Year of Publication: 
2013
Series/Report no.: 
CESifo Working Paper 4232
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 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.
Subjects: 
large panels
lagged dependent variable
cross sectional dependence
coefficient heterogeneity
estimation and inference
common correlated effects
unobserved common factors
JEL: 
C31
C33
Document Type: 
Working Paper

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