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

Ron Mittelhammer () and George Judge ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

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: binary choice models and estimators; conditional moment equations; squared error loss; Cressie-Read statistic; information theoretic methods; minimum power divergence (search for similar items in EconPapers)
Date: 2008-07-08
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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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