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

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 co-variates 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.

Estimation of heterogeneous panels with structural breaks

“…Kapetanios, Pesaran and Yamagata (2011) show that the CCE estimator can be extended to the case of nonstationary unobserved common factors. Additionally, the CCE approach is also shown to be applicable to situations of spatial and other forms of weak cross-sectional dependent errors Chudik, Pesaran and Tosetti, 2011), and heterogenous dynamic panel data models with weakly exogenous regressors (Chudik and Pesaran, 2013). 2 However, this literature assumes that the slope coe¢ cients are constant over time.…”

Factor Models in Panels with Cross-sectional Dependence: An Application to the Extended SIPRI Military Expenditure Data

“…Pesaran (2006) provides more details and the generalisation to the dynamic case, which we use below, is provided in Chudik and Pesaran (2015a). Note that the covariance of s t with u it declines with N, so for N large we can treat s t as exogenous.…”

Dynamic Linear Panel Regression Models With Interactive Fixed Effects

“…Harding and Lamarche (2009;, Kapetanios, Pesaran and Yamagata (2011), Chudik, Pesaran and Tosetti (2011), and Chudik and Pesaran (2013. 3 The LS estimator is sometimes called "concentrated" least squares estimator in the literature, and in an earlier version of the paper we referred to it as the "Gaussian Quasi Maximum Likelihood Estimator", since LS estimation is equivalent to maximizing a conditional Gaussian likelihood function.…”