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

Ron Mittelhammer () and George Judge ()

No 37759, CUDARE Working Papers from University of California, Berkeley, Department of Agricultural and Resource Economics

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.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Pages: 31
Date: 2008-07
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Related works:
Journal Article: A Minimum Power Divergence Class of CDFs and Estimators for the Binary Choice Model (2009)
Working Paper: A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ucbecw:37759

DOI: 10.22004/ag.econ.37759

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