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Econometrics 2019, 7(1), 7;

Panel Data Estimation for Correlated Random Coefficients Models

Department of Economics, University of Southern California, Los Angeles, CA 90089, USA
Department of Quantitative Finance, NTHU and WISE, Xiamen University, Xiamen 361005, China
Department of Economics, Texas A&M University, College Station, TX 77843, USA
Department of Economics, University at Albany, SUNY, Albany, NY 12222, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 30 January 2018 / Revised: 13 January 2019 / Accepted: 23 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Celebrated Econometricians: Peter Phillips)
Full-Text   |   PDF [777 KB, uploaded 1 February 2019]


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. View Full-Text
Keywords: panel data; correlated random coefficients; efficiency bound panel data; correlated random coefficients; efficiency bound
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Hsiao, C.; Li, Q.; Liang, Z.; Xie, W. Panel Data Estimation for Correlated Random Coefficients Models. Econometrics 2019, 7, 7.

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