Working Paper Series
Information Criterion and Estimation of Misspecified Qualitative Choice Models
Abstract: This paper investigates misspecified estimation and model
selection criteria derived from the "Information Criterion (see Akaike
(1973))" for qualitative choice models. Four estimators for the
"Information Criterion" are derived for general qualitative choice models.
Two of these estimators were previously derived by Akaike (1973) and Chow
(1981) for arbitrary likelihood functions. The new estimators are derived
by taking analytic expectations of the log likelihood function. A number of
Monte Carlo experiments are performed using binominal logit models to
investigate the behavior of the Information Criterion estimators with
realistic sample sizes. The new analytic estimators are more accurate than
the more general estimators , but they do not always perform as well in
minimizing prediction or estimation error. Monte Carlo results also show
that the usual asymptotic distribution properties of the maximum likelihood
estimator are poor approximations for sample sizes as large as 1,000
observations with only two variables.
Keywords: Model selection criteria; misspecified estimation; information criterion; qualitative choice models; (follow links to similar papers)
JEL-Codes: C51; C52; (follow links to similar papers)
59 pages, August 1984
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