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

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  • Mittelhammer, Ron C Dr.
  • Judge, George G.

Abstract

The Cressie-Read (CR) family of power divergence measures is used to identify a new class of statistical models and estimators for competing explanations of the data in binary choice models. A large flexible class of cumulative distribution functions and associated probability density functions emerge that subsumes the conventional logit model, and forms the basis for a large set of estimation alternatives to traditional logit and probit methods. Asymptotic properties of estimators are identified, and sampling experiments are used to provide a basis for gauging the finite sample performance of the estimators in this new class of statistical models.

Suggested Citation

  • Mittelhammer, Ron C Dr. & Judge, George G., 2008. "A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7bc2828q, Department of Agricultural & Resource Economics, UC Berkeley.
    • Handle: RePEc:cdl:agrebk:qt7bc2828q
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    References listed on IDEAS

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    More about this item

    Keywords

    binary choice models and estimators; conditional moment equations; squared error loss; Cressie-Read statistic; information theoretic methods; minimum power divergence;
    All these keywords.

    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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