Template-Type: ReDIF-Article 1.0
Author-Name:  Ron Mittelhammer
Author-Email:  mittelha@wsu.edu
Author-Workplace-Name: Washington State University
Author-Name:  George Judge
Author-Email: judge@are.berkeley.edu
Author-Workplace-Name: University of California, Berkeley
Title:  A Minimum Power Divergence Class of CDFs and Estimators
for the Binary Choice Model
Abstract:  This paper uses information theoretic methods to introduce a new class of probability distributions and estimators for competing explanations of the data in the binary choice model. No explicit parameterization of the function connecting the data to the Bernoulli probabilities is stated in the specification of the statistical model. A large class of probability density functions emerges including the conventional logit model. The new class of statistical models and estimators requires minimal a priori model structure and non-sample information, and provides a range of model and estimator extensions. An empirical example is included to reflect the applicability of these methods.
Classification-JEL: C10, C2
Keywords: Semiparametric Binary Estimators, Conditional Moment Equations, Squared Error Loss, Cressie-Read Statistic, Information Theoretic Methods
Journal: International Econometric Review
Pages: 33-49
Volume: 1
Issue: 1
Year: 2009
Month: April
File-URL: http://www.era.org.tr/makaleler/6010030.pdf
File-Format: Application/pdf
Handle: RePEc:erh:journl:v:1:y:2009:i:1:p:33-49