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Bayesian Estimation and Smoothing of the Baseline Hazard in Discrete Time Duration Models

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  • Michele Campolieti

Abstract

This paper proposes a Bayesian approach for estimating and smoothing the baseline hazard in a discrete time hazard model. The hazard model is specified as a multiperiod probit model and estimated using a Gibbs sampler with data augmentation. The baseline hazard specification is smoothed using the smoothness priors introduced by Shiller (1973). The methods proposed in this paper are then used to study the effect of Canadian Unemployment Insurance eligibility rules on employment durations from New Brunswick, Canada. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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  • Michele Campolieti, 2000. "Bayesian Estimation and Smoothing of the Baseline Hazard in Discrete Time Duration Models," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 685-694, November.
  • Handle: RePEc:tpr:restat:v:82:y:2000:i:4:p:685-694
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    Cited by:

    1. Michele Campolieti, 2003. "On the estimation of hazard models with flexible baseline hazards and nonparametric unobserved heterogeneity," Economics Bulletin, AccessEcon, vol. 3(24), pages 1-10.
    2. Campolieti, Michele, 2009. "An Analysis of Unemployment Incidence and Duration: Some New Evidence from Canada," CLSSRN working papers clsrn_admin-2009-14, Vancouver School of Economics, revised 02 Feb 2009.
    3. repec:ebl:ecbull:v:3:y:2003:i:24:p:1-10 is not listed on IDEAS

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