Comparison of parametric and semi-parametric binary response models

Arbeitspapier

A 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2013-08

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions ; Bayesian Analysis: General
• Subject: Semi-parametric binary response models ; Markov Chain Monte Carlo algorithms ; Kernel densities ; Optimal bandwidth ; Receiver operating characteristic curve

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Shen, Xiangjin ; Li, Shiliang ; Tsurumi, Hiroki
• Event: Veröffentlichung
• (who): Rutgers University, Department of Economics
• (where): New Brunswick, NJ
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/94225
• Last update: 10.03.2025, 11:44 AM CET