Stein-like Shrinkage Estimation of Panel Data Models with Common Correlated Effects

Bai Huang, Tae Hwy Lee and Aman Ullah

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, 2019, vol. 40A, pp 249-274 from Emerald Group Publishing Limited

Abstract: This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case, the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.

Keywords: Endogeneity; panel data; fixed effect; common correlated effects; shrinkage; model averaging; local asymptotics; Hausman test; C13; C33; C52 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Stein-like Shrinkage Estimation of Panel Data Models with Common Correlated Effects (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040a011

DOI: 10.1108/S0731-90532019000040A011

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