BAYESIAN ANALYSIS OF MISSPECIFIED MODELS WITH FIXED EFFECTS

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

One way to control for the heterogeneity in panel data is to allow for time-invariant, individual specific parameters. This fixed effect approach introduces many parameters into the model which causes the "incidental parameter problem": the maximum likelihood estimator is in general inconsistent. Woutersen (2001) shows how to approximately separate the parameters of interest from the fixed effects using a reparametrization. He then shows how a Bayesian method gives a general solution to the incidental parameter for correctly specified models. This paper extends Woutersen (2001) to misspecified models. Following White (1982), we assume that the expectation of the score of the integrated likelihood is zero at the true values of the parameters. We then derive the conditions under which a Bayesian estimator converges at rate N where N is the number of individuals. Under these conditions, we show that the variance-covariance matrix of the Bayesian estimator has the form of White (1982). We illustrate our approach by the dynamic linear model with fixed effects and a duration model with fixed effects.

Original languageEnglish (US)
Title of host publicationMaximum Likelihood Estimation of Misspecified Models
Subtitle of host publicationTwenty Years Later
Pages235-249
Number of pages15
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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