Stein-like Common Correlated Effects Estimation Under Structural Breaks

Shahnaz Parsaeian
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Shahnaz Parsaeian: Department of Economics, University of Kansas, Lawrence, KS 66045

No 202409, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined estimator of the CCE full-sample estimator (i.e., estimation using both the pre-break and post-break observations) and the CCE post-break estimator (i.e., estimation using only the post-break sample observations). The proposed Stein-like combined estimator benefits from exploiting the pre-break sample observations. We derive the optimal combination weight by minimizing the asymptotic risk. We show the superiority of the CCE Stein-like combined estimator over the CCE post-break estimator in terms of the asymptotic risk. Further, we establish the asymptotic properties of the CCE mean group Stein-like combined estimator. The finite sample performance of our proposed estimator is investigated using Monte Carlo experiments and an empirical application of predicting the output growth of industrialized countries.

Keywords: Common correlated effects; Cross-sectional dependence; Heterogeneous panels; Structural breaks. (search for similar items in EconPapers)
JEL-codes: C13 C23 C33 (search for similar items in EconPapers)
Date: 2024-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-inv
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