Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2009
Abstract
This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference can be enormous.
Keywords
Asymptotics, Bootstrap, Quasi-MLE, Transformed panels, Variance-covariance matrix estimate
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
30
Publisher
SMU Economics and Statistics Working Paper Series, No. 03-2009
City or Country
Singapore
Citation
SU, Liangjun and YANG, Zhenlin.
Asymptotics and Bootstrap for Transformed Panel Data Regressions. (2009). 1-30.
Available at: https://ink.library.smu.edu.sg/soe_research/1138
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.