Publication Type

Journal Article

Publication Date

4-2016

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

ISSN

0747-4938

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1080/07474938.2015.1122235

Included in

Econometrics Commons

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