A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models
AbstractThe 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.
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Bibliographic InfoPaper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number qt7bc2828q.
Date of creation: 08 Jul 2008
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binary choice models and estimators; conditional moment equations; squared error loss; Cressie-Read statistic; information theoretic methods; minimum power divergence;
Other versions of this item:
- Mittelhammer, Ronald C. & Judge, George G, 2008. "A minimum power divergence class of CDFs and estimators for binary choice models," CUDARE Working Paper Series 1059, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
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