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A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models AgEcon
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
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Minimum Divergence Moment Based Binary Response Models: Estimation and Inference AgEcon
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
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The information theoretic foundations of a probabilistic and predictive micro and macro economics AgEcon
Judge, George G..
Despite the productive efforts of economists, the disequilibrium nature of the economic system and imprecise predictions persist. One reason for this outcome is that traditional econometric models and estimation and inference methods cannot provide the necessary quantitative information for the causal influence-dynamic micro and macro questions we need to ask given the noisy indirect effects data we use. To move economics in the direction of a probabilistic and causal based predictive science, in this paper information theoretic estimation and inference methods are suggested as a basis for understanding and making predictions about dynamic micro and macro economic processes and systems.
Tipo: Working Paper Palavras-chave: Information theoretic methods; State space models; First order Markov processes; Inverse problems; Dynamic economic systems; Research Methods/ Statistical Methods; C40; C51.
Ano: 2012 URL: http://purl.umn.edu/122890
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