Asymptotics and bootstrap for random-effects panel data transformation models
研究了随机效应面板数据变换模型中准极大似然估计量的渐近性质，提出自助法估计方差协方差矩阵，蒙特卡洛模拟显示有限样本表现良好。
This paper 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.