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Random effects panel data models with known heteroskedasticity

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  • Julius Schäper
  • Rainer Winkelmann

Abstract

The paper considers two estimators for the linear random effects panel data model with known heteroskedasticity. Examples where heteroskedasticity can be treated as given include panel regression with averaged data, meta regression and the linear probability model. While one estimator builds on the additive random effects assumption, the other, which is simpler to implement in standard software, assumes that the random effect is multiplied by the heteroskedastic standard deviation. Simulation results show that substantial efficiency gains can be realized with either of the two estimators, even in case of misspecification of the scedastic function. Correct confidence interval coverage is obtained if clustered standard errors are used. Efficiency gains are also evident in an illustrative meta-regression application estimating the effect of study design features on loss aversion coefficients.

Suggested Citation

  • Julius Schäper & Rainer Winkelmann, 2024. "Random effects panel data models with known heteroskedasticity," ECON - Working Papers 445, Department of Economics - University of Zurich, revised Sep 2024.
    • Handle: RePEc:zur:econwp:445
    as

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    File URL: https://www.zora.uzh.ch/id/eprint/259907/7/econwp445.pdf
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    References listed on IDEAS

    as
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    4. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    5. Wansbeek, Tom, 1989. "An Alternative Heteroscedastic Error Components Model," Econometric Theory, Cambridge University Press, vol. 5(02), pages 326-326, August.
    6. Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-753, November.
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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