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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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Empirical Evidence Concerning the Finite Sample Performance of EL-Type Structural Equation Estimation and Inference Methods AgEcon
Mittelhammer, Ronald C.; Judge, George G.; Schoenberg, Ron.
This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the literature that traditional and non-traditional asymptotically efficient estimators based on moment equations may, for the relatively small sample sizes usually encountered in econometric practice, have relatively large biases and/or variances and provide an inadequate basis for estimation and inference. Given this uncertainty we use a range of data sampling processes and Monte Carlo sampling procedures to accumulate finite sample empirical evidence concerning...
Tipo: Working or Discussion Paper Palavras-chave: Unbiased moment based estimation and inference; Empirical likelihood; Empirical exponential likelihood; Semiparametric models; Conditional estimating equations; Finite sample bias and precision; Squared error loss; Instrumental conditioning variables; Research Methods/ Statistical Methods; C10; C24.
Ano: 2003 URL: http://purl.umn.edu/25090
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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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