Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/94225
Authors: 
Shen, Xiangjin
Li, Shiliang
Tsurumi, Hiroki
Year of Publication: 
2013
Series/Report no.: 
Working Papers, Department of Economics, Rutgers, The State University of New Jersey 2013-08
Abstract: 
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.
Subjects: 
Semi-parametric binary response models
Markov Chain Monte Carlo algorithms
Kernel densities
Optimal bandwidth
Receiver operating characteristic curve
JEL: 
C14
C35
C11
Document Type: 
Working Paper

Files in This Item:
File
Size
584.88 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.