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Panel Data Estimation for Correlated Random Coefficients Models

Cheng Hsiao (), Qi Li (), Zhongwen Liang () and Wei Xie ()
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Cheng Hsiao: Department of Economics, University of Southern California, Los Angeles, CA 90089, USA
Qi Li: Department of Economics, Texas A&M University, College Station, TX 77843, USA
Zhongwen Liang: Department of Economics, University at Albany, SUNY, Albany, NY 12222, USA
Wei Xie: Department of Economics, University of Southern California, Los Angeles, CA 90089, USA

Econometrics, 2019, vol. 7, issue 1, 1-18

Abstract: This paper considers methods of estimating a static correlated random coefficient model with panel data. We mainly focus on comparing two approaches of estimating unconditional mean of the coefficients for the correlated random coefficients models, the group mean estimator and the generalized least squares estimator. For the group mean estimator, we show that it achieves Chamberlain (1992) semi-parametric efficiency bound asymptotically. For the generalized least squares estimator, we show that when T is large, a generalized least squares estimator that ignores the correlation between the individual coefficients and regressors is asymptotically equivalent to the group mean estimator. In addition, we give conditions where the standard within estimator of the mean of the coefficients is consistent. Moreover, with additional assumptions on the known correlation pattern, we derive the asymptotic properties of panel least squares estimators. Simulations are used to examine the finite sample performances of different estimators.

Keywords: panel data; correlated random coefficients; efficiency bound (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: 2019
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