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

Michele Campolieti
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Michele Campolieti: Division of Management, University of Toronto at Scarborough and Center for Industrial Relations, University of Toronto, Postal: Division of Management, University of Toronto at Scarborough and Center for Industrial Relations, University of Toronto

Journal of Applied Econometrics, 2001, vol. 16, issue 1, 1-22

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.

Date: 2001
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