Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238783 
Year of Publication: 
2009
Citation: 
[Journal:] International Econometric Review (IER) [ISSN:] 1308-8815 [Volume:] 1 [Issue:] 1 [Publisher:] Econometric Research Association (ERA) [Place:] Ankara [Year:] 2009 [Pages:] 33-49
Publisher: 
Econometric Research Association (ERA), Ankara
Abstract: 
This paper uses information theoretic methods to introduce a new class of probability distributions and estimators for competing explanations of the data in the binary choice model. No explicit parameterization of the function connecting the data to the Bernoulli probabilities is stated in the specification of the statistical model. A large class of probability density functions emerges including the conventional logit model. The new class of statistical models and estimators requires minimal a priori model structure and non-sample information, and provides a range of model and estimator extensions. An empirical example is included to reflect the applicability of these methods.
Subjects: 
Semiparametric Binary Estimators
Conditional Moment Equations
Squared Error Loss
Cressie-Read Statistic
Information Theoretic Methods
JEL: 
C10
C2
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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