Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures

Shiyun Cao and Qiankun Zhou
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Shiyun Cao: School of Science, Guangxi University of Science and Technology, Liuzhou 545006, China

Econometrics, 2022, vol. 10, issue 3, 1-27

Abstract: In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the CCEMG estimator is asymptotically normally distributed. The theoretical findings were supported for small samples by an extensive simulation study, showing that the CCE estimators are robust to a wide variety of data generation processes. Empirical findings suggest that the CCE estimation is widely applicable to models with non-stationary factors. The proposed procedure is also illustrated by an empirical application to analyze the U.S. cigar dataset.

Keywords: dynamic panel models; cross-sectional dependence; non-stationary; common factors; common correlated effects (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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