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Comparison of Parametric and Semi-Parametric Binary Response Models

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  • Xiangjin Shen

    ()
    (Rutgers University, Economics Department)

  • Shiliang Li

    ()
    (Rutgers University, Statistics Department)

  • Hiroki Tsurumi

    ()
    (Rutgers University, Economics Department)

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    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.

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    Bibliographic Info

    Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201308.

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    Length: 20 pages
    Date of creation: 12 Jul 2013
    Date of revision:
    Handle: RePEc:rut:rutres:201308

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    Keywords: Semi-parametric binary response models; Markov Chain Monte Carlo algorithms; Kernel densities; Optimal bandwidth; Receiver operating characteristic curve;

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    1. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data," Annals of the Institute of Statistical Mathematics, Springer, vol. 53(1), pages 11-30, March.
    2. Hutchens, Robert M, 1989. "Seniority, Wages and Productivity: A Turbulent Decade," Journal of Economic Perspectives, American Economic Association, vol. 3(4), pages 49-64, Fall.
    3. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer, vol. 72(3), pages 327-346, September.
    4. Kottas A. & Gelfand A.E., 2001. "Bayesian Semiparametric Median Regression Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1458-1468, December.
    5. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
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