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

Copyright Owner and License

Authors

Included in

Econometrics Commons

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