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

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  • Ron Mittelhammer

    ()
    (Washington State University)

  • George Judge

    ()
    (University of California, Berkeley)

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.

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File URL: http://www.era.org.tr/makaleler/6010030.pdf
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Bibliographic Info

Article provided by Econometric Research Association in its journal International Econometric Review.

Volume (Year): 1 (2009)
Issue (Month): 1 (April)
Pages: 33-49

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Handle: RePEc:erh:journl:v:1:y:2009:i:1:p:33-49

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Related research

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

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