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	Provedor de dados 31 (2) Autor Judge, George G. (2) Mittelhammer, Ronald C. (2) Cardell, N. Scott (1) Miller, Douglas J. (1) Palavra-chave Conditional moment equations (2) Cressie-Read statistic (2) Information theoretic methods (2) Research Methods/ Statistical Methods (2) Squared error loss (2) Binary choice models and estimators (1) C10 (1) C2 (1) Finite sample bias and precision (1) Minimum power divergence (1) Nonparametric binary response models and estimators (1) Response variables (1) Mais... Tipo do documento Working or Discussion Paper (2) Ano 2008 (1) 2005 (1) País United States (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models 31 Mittelhammer, Ronald C.; Judge, George G.. 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. Tipo: Working or Discussion Paper Palavras-chave: Binary choice models and estimators; Conditional moment equations; Squared error loss; Cressie-Read statistic; Information theoretic methods; Minimum power divergence; Research Methods/ Statistical Methods. Ano: 2008 URL: http://purl.umn.edu/37759 Minimum Divergence Moment Based Binary Response Models: Estimation and Inference 31 Mittelhammer, Ronald C.; Judge, George G.; Miller, Douglas J.; Cardell, N. Scott. This paper introduces a new class of estimators based on minimization of the Cressie-Read (CR) power divergence measure for binary choice models, where neither a parameterized distribution nor a parameterization of the mean is specified explicitly in the statistical model. By incorporating sample information in the form of conditional moment conditions and estimating choice probabilities by optimizing a member of the set of divergence measures in the CR family, a new class of nonparametric estimators evolves that requires less a priori model structure than conventional parametric estimators such as probit or logit. Asymptotic properties are derived under general regularity conditions and finite sampling properties are illustrated by Monte Carlo sampling... Tipo: Working or Discussion Paper Palavras-chave: Nonparametric binary response models and estimators; Conditional moment equations; Finite sample bias and precision; Squared error loss; Response variables; Cressie-Read statistic; Information theoretic methods; Research Methods/ Statistical Methods; C10; C2. Ano: 2005 URL: http://purl.umn.edu/25020 Registros recuperados: 2 Primeira ... 1 ... Última

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