Chen, Pian and Velamuri, Malathi (2009): Misspecification and Heterogeneity in SingleIndex, Binary Choice Models.

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Abstract
We propose a nonparametric approach for estimating singleindex, 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 kernel regression of the binary choice variable on the single index estimated in the first step. The estimated probability functions for different demographic groups indicate that the conventional dummy variable approach cannot fully capture heterogeneous effects across groups. Using both simulated and labor market data, we demonstrate the merits of this new approach in solving model misspecification and heterogeneity problems.
Item Type:  MPRA Paper 

Original Title:  Misspecification and Heterogeneity in SingleIndex, Binary Choice Models 
Language:  English 
Keywords:  Probit; Logit; Sliced Inverse Regression; categorical variables; treatment heterogeneity 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C21  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions 
Item ID:  15722 
Depositing User:  Malathi Velamuri 
Date Deposited:  15. Jun 2009 05:52 
Last Modified:  22. Apr 2015 16:20 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/15722 