Comparison of Parametric and Semi-Parametric Binary Response Models

Xiangjin Shen, Shiliang Li () and Hiroki Tsurumi ()
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Shiliang Li: Rutgers University, Statistics Department
Hiroki Tsurumi: Rutgers University, Economics Department

Departmental Working Papers from Rutgers University, Department of Economics

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.

Keywords: Semi-parametric binary response models; Markov Chain Monte Carlo algorithms; Kernel densities; Optimal bandwidth; Receiver operating characteristic curve (search for similar items in EconPapers)
JEL-codes: C11 C14 C35 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2013-07-12
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201308

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