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Comparison of Parametric and Semi-Parametric Binary Response Models

Author

Listed:
  • Xiangjin Shen

    (Rutgers University, Economics Department)

  • Shiliang Li

    (Rutgers University, Statistics Department)

  • Hiroki Tsurumi

    (Rutgers University, Economics Department)

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.

Suggested Citation

  • Xiangjin Shen & Shiliang Li & Hiroki Tsurumi, 2013. "Comparison of Parametric and Semi-Parametric Binary Response Models," Departmental Working Papers 201308, Rutgers University, Department of Economics.
    • Handle: RePEc:rut:rutres:201308
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    File URL: http://www.sas.rutgers.edu/virtual/snde/wp/2013-08.pdf
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    References listed on IDEAS

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    1. Bester, Helmut & Petrakis, Emmanuel, 2003. "Wages and productivity growth in a competitive industry," Journal of Economic Theory, Elsevier, vol. 109(1), pages 52-69, March.
    2. Barbara J. McNeil & James A. Hanley, 1984. "Statistical Approaches to the Analysis of Receiver Operating Characteristic (ROC) Curves," Medical Decision Making, , vol. 4(2), pages 137-150, June.
    3. Roger Klein & Francis Vella, 2009. "A semiparametric model for binary response and continuous outcomes under index heteroscedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 735-762.
    4. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    5. 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.
    6. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
    7. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    8. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 11-30, March.
    9. 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.
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    Cited by:

    1. Gregory Connor & Thomas Flavin, 2013. "Irish Mortgage Default Optionality," Economics Department Working Paper Series n243-13.pdf, Department of Economics, National University of Ireland - Maynooth.

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    More about this item

    Keywords

    Semi-parametric binary response models; Markov Chain Monte Carlo algorithms; Kernel densities; Optimal bandwidth; Receiver operating characteristic curve;
    All these keywords.

    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

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