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Panel Data Estimation for Correlated Random Coefficients Models

Author

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  • Cheng Hsiao

    (Department of Economics, University of Southern California, Los Angeles, CA 90089, USA
    Department of Quantitative Finance, NTHU and WISE, Xiamen University, Xiamen 361005, China
    These authors contributed equally to this work.)

  • Qi Li

    (Department of Economics, Texas A&M University, College Station, TX 77843, USA
    These authors contributed equally to this work.)

  • Zhongwen Liang

    (Department of Economics, University at Albany, SUNY, Albany, NY 12222, USA
    These authors contributed equally to this work.)

  • Wei Xie

    (Department of Economics, University of Southern California, Los Angeles, CA 90089, USA
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Cheng Hsiao & Qi Li & Zhongwen Liang & Wei Xie, 2019. "Panel Data Estimation for Correlated Random Coefficients Models," Econometrics, MDPI, vol. 7(1), pages 1-18, February.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:1:p:7-:d:202457
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    References listed on IDEAS

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