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.
Asymptotics, error components bootstrap, quasi-MLE, Transformed panels, random-effects, robust VC matrix estimation
Taylor & Francis: STM, Behavioural Science and Public Health Titles
SU, Liangjun and YANG, Zhenlin.
Asymptotics and bootstrap for random-effects panel data transformation models. (2016). Econometric Reviews. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1920
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.