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Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior

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  • Michele Campolieti

    (Division of Management, University of Toronto at Scarborough and Center for Industrial Relations, University of Toronto)

Abstract

This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non-parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:jae:japmet:v:16:y:2001:i:1:p:1-22
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    References listed on IDEAS

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    Cited by:

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    3. Pankaj C. Patel & Mike G. Tsionas, 2022. "Learning‐by‐lending and learning‐by‐repaying: A two‐sided learning model for defaults on Small Business Administration loans," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(4), pages 906-919, June.
    4. 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 of Labor Economics (IZA).
    5. Munkin, Murat K., 2011. "The Endogenous Sequential Probit model: An application to the demand for hospital utilization," Economics Letters, Elsevier, vol. 112(2), pages 182-185, August.
    6. Mojtaba Ganjali & T. Baghfalaki & D. Berridge, 2014. "A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 24-41, April.
    7. Ando, Tomohiro, 2009. "Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1925-1939, April.
    8. Bhattacharjee, Arnab & Bhattacharjee, Madhuchhanda, 2007. "Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing," MPRA Paper 3938, University Library of Munich, Germany.
    9. Lau, John W., 2006. "Bayesian semi-parametric modeling for mixed proportional hazard models with right censoring," Statistics & Probability Letters, Elsevier, vol. 76(7), pages 719-728, April.
    10. Isachenkova, N. & Weeks, M., 2008. "Acquisition, Insolvency and Managers in UK Small Companies," Cambridge Working Papers in Economics 0838, Faculty of Economics, University of Cambridge.
    11. Mingliang Li, 2006. "High school completion and future youth unemployment: new evidence from High School and Beyond," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 23-53, January.
    12. Zamanzadeh, Akbar & Chan, Marc K. & Ehsani, Mohammad Ali & Ganjali, Mojtaba, 2020. "Unemployment duration, Fiscal and monetary policies, and the output gap: How do the quantile relationships look like?," Economic Modelling, Elsevier, vol. 91(C), pages 613-632.

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