Comparison of Parametric and Semi-Parametric Binary Response Models
AbstractA Bayesian semi-parametric estimation of the binary response model using Markov Chain Monte Carlo algorithms is proposed. The performances of the parametric and semi-parametric models are presented. The mean squared errors, receiver operating characteristic curve, and the marginal effect are used as the model selection criteria. Simulated data and Monte Carlo experiments show that unless the binary data is extremely unbalanced the semi-parametric and parametric models perform equally well. However, if the data is extremely unbalanced the maximum likelihood estimation does not converge whereas the Bayesian algorithms do. An application is also presented.
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Bibliographic InfoPaper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201308.
Length: 20 pages
Date of creation: 12 Jul 2013
Date of revision:
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Semi-parametric binary response models; Markov Chain Monte Carlo algorithms; Kernel densities; Optimal bandwidth; Receiver operating characteristic curve;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-20 (All new papers)
- NEP-DCM-2013-07-20 (Discrete Choice Models)
- NEP-ECM-2013-07-20 (Econometrics)
- NEP-ORE-2013-07-20 (Operations Research)
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