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Asymptotics and bootstrap for random-effects panel data transformation models

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  • Liangjun Su
  • Zhenlin Yang

Abstract

This article investigates the asymptotic properties of quasi-maximum likelihood (QML) estimators for random-effects panel data transformation models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoskedasticity, and simple model structure. We develop a QML-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the QML estimators, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance (VC) matrix. Monte Carlo results reveal that the QML estimators perform well in finite samples, and that the gains by using the robust VC matrix estimate for inference can be enormous.

Suggested Citation

  • Liangjun Su & Zhenlin Yang, 2018. "Asymptotics and bootstrap for random-effects panel data transformation models," Econometric Reviews, Taylor & Francis Journals, vol. 37(6), pages 602-625, July.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:6:p:602-625
    DOI: 10.1080/07474938.2015.1122235
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