Misspecification and Heterogeneity in Single-Index, Binary Choice Models
AbstractWe propose a nonparametric approach for estimating 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 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 15722.
Date of creation: May 2009
Date of revision:
Probit; Logit; Sliced Inverse Regression; categorical variables; treatment heterogeneity;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-06-17 (All new papers)
- NEP-DCM-2009-06-17 (Discrete Choice Models)
- NEP-ECM-2009-06-17 (Econometrics)
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- Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
- Guido M. Imbens & Jeffrey M. Wooldridge, 2008.
"Recent Developments in the Econometrics of Program Evaluation,"
NBER Working Papers
14251, National Bureau of Economic Research, Inc.
- Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
- Imbens, Guido & Wooldridge, Jeffrey M., 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
- Imbens, Guido W. & Wooldridge, Jeffrey M., 2008. "Recent Developments in the Econometrics of Program Evaluation," IZA Discussion Papers 3640, Institute for the Study of Labor (IZA).
- Guido Imbens & Jeffrey Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
- LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
- James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998.
"Characterizing Selection Bias Using Experimental Data,"
Econometric Society, vol. 66(5), pages 1017-1098, September.
- James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
- Klein, Roger W & Spady, Richard H, 1993.
"An Efficient Semiparametric Estimator for Binary Response Models,"
Econometric Society, vol. 61(2), pages 387-421, March.
- Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
- Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
- Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
- Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
- Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
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