A Minimum Power Divergence Class of CDFs and Estimators for the Binary Choice Model

Year 2009, Volume: 1 Issue: 1, 33 - 49, 01.04.2009

Ron Mittelhammer George Judge

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.

Keywords

Semiparametric Binary Estimators, Conditional Moment Equations, Squared Error Loss, Cressie-Read Statistic, Information Theoretic Methods

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