Misspecification and Heterogeneity in Single-Index, Binary Choice Models
نویسندگان
چکیده
منابع مشابه
Misspecification and Heterogeneity in Single-Index, Binary Choice Models
We propose a nonparametric approach for estimating single-index, binarychoice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without specifying a parametric probability function a priori; second, we estimate the unknown probability function using kerne...
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We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...
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In semiparametric binary response models, support conditions on the regressors are required to guarantee point identification of the parameter of interest. For example, one regressor is usually assumed to have continuous support conditional on the other regressors. In some instances, such conditions have precluded the use of these models; in others, practitioners have failed to consider whether...
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No retail store choice model, no matter how many relevant variables it might include, can realistically expect to model all the variation in store choice. There are always some variables that are left out, because they are difficult to measure, they have not yet been conceptualized in theory, or their estimated parameter stability suffers when an excessive number of predictors are included. Bec...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2009
ISSN: 1556-5068
DOI: 10.2139/ssrn.1393062