Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2018
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.
Keywords
Asymptotics, error components bootstrap, quasi-MLE, Transformed panels, random-effects, robust VC matrix estimation
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Reviews
Volume
37
Issue
6
First Page
602
Last Page
625
ISSN
0747-4938
Identifier
10.1080/07474938.2015.1122235
Publisher
Taylor & Francis
Citation
SU, Liangjun and YANG, Zhenlin.
Asymptotics and bootstrap for random-effects panel data transformation models. (2018). Econometric Reviews. 37, (6), 602-625.
Available at: https://ink.library.smu.edu.sg/soe_research/1920
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.
Additional URL
https://doi.org/10.1080/07474938.2015.1122235