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Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application

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  • Paserman, M. Daniele

    () (Boston University)

Abstract

This paper describes a semiparametric Bayesian method for analyzing duration data. The proposed estimator specifies a complete functional form for duration spells, but allows flexibility by introducing an individual heterogeneity term, which follows a Dirichlet mixture distribution. I show how to obtain predictive distributions for duration data that correctly account for the uncertainty present in the model. I also directly compare the performance of the proposed estimator with Heckman and Singer's (1984) Non Parametric Maximum Likelihood Estimator (NPMLE). The methodology is applied to the analysis of youth unemployment spells. Compared to the NPMLE, the proposed estimator reflects more accurately the uncertainty surrounding the heterogeneity distribution.

Suggested Citation

  • Paserman, M. Daniele, 2004. "Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application," IZA Discussion Papers 996, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp996
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    References listed on IDEAS

    as
    1. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    2. Meyer, Bruce D, 1990. "Unemployment Insurance and Unemployment Spells," Econometrica, Econometric Society, vol. 58(4), pages 757-782, July.
    3. Chamberlain, Gary & Imbens, Guido W, 2003. "Nonparametric Applications of Bayesian Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 12-18, January.
    4. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    5. Ham, John C & LaLonde, Robert J, 1996. "The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training," Econometrica, Econometric Society, vol. 64(1), pages 175-205, January.
    6. Ham, John C & Rea, Samuel A, Jr, 1987. "Unemployment Insurance and Male Unemployment Duration in Canada," Journal of Labor Economics, University of Chicago Press, vol. 5(3), pages 325-353, July.
    7. Mark Dynarski & Steven M. Sheffrin, 1987. "Consumption and Unemployment," The Quarterly Journal of Economics, Oxford University Press, vol. 102(2), pages 411-428.
    8. Michele Campolieti, 2001. "Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(1), pages 1-22.
    9. Blank, Rebecca M., 1989. "Analyzing the length of welfare spells," Journal of Public Economics, Elsevier, vol. 39(3), pages 245-273, August.
    10. Ruggiero, Michele, 1994. "Bayesian semiparametric estimation of proportional hazards models," Journal of Econometrics, Elsevier, vol. 62(2), pages 277-300, June.
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    More about this item

    Keywords

    duration data; Dirichlet process; Bayesian inference; Markov chain Monte Carlo simulation;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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