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

Author

Listed:
  • 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.

Suggested Citation

  • Ron Mittelhammer & George Judge, 2009. "A Minimum Power Divergence Class of CDFs and Estimators for the Binary Choice Model," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 33-49, April.
  • Handle: RePEc:erh:journl:v:1:y:2009:i:1:p:33-49
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    More about this item

    Keywords

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

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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