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

Author

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  • Shiyun Cao

    (School of Science, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Qiankun Zhou

    (Department of Economics, Louisiana State University, Baton Rouge, LA 70803, USA)

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.

Suggested Citation

  • Shiyun Cao & Qiankun Zhou, 2022. "Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures," Econometrics, MDPI, vol. 10(3), pages 1-27, August.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:3:p:29-:d:886156
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