A COMPARATIVE STUDY OF PURE AND PRETEST ESTIMATORS FOR A POSSIBLY MISSPECIFIED TWO-WAY ERROR COMPONENT MODEL

Badi H. Baltagi, Georges Bresson, Alain Pirotte

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In the spirit of White's (1982) paper, this paper examines the consequences of model misspecification using a panel data regression model. Maximum likelihood, random and fixed effects estimators are compared using Monte Carlo experiments under normality of the disturbances but with a possibly misspecified variance-covariance matrix. We show that the correct GLS (ML) procedure is always the best according to MSE performance, but the researcher does not have perfect foresight on the true form of the variance covariance matrix. In this case, we show that a pretest estimator is a viable alternative given that its performance is a close second to correct GLS (ML) whether the true specification is a two-way, a one-way error component model or a pooled regression model. Incorrect GLS, ML or fixed effects estimators may lead to a big loss in MSE.

Original languageEnglish (US)
Title of host publicationMaximum Likelihood Estimation of Misspecified Models
Subtitle of host publicationTwenty Years Later
Pages1-27
Number of pages27
DOIs
StatePublished - Dec 1 2003
Externally publishedYes

Publication series

NameAdvances in Econometrics
Volume17
ISSN (Print)0731-9053

ASJC Scopus subject areas

  • Economics and Econometrics

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