EconStor >
Forschungsinstitut zur Zukunft der Arbeit (IZA), Bonn >
IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA) >

Please use this identifier to cite or link to this item:
Title:Bayesian inference for duration data with unobserved and unknown heterogeneity : Monte Carlo evidence and an application PDF Logo
Authors:Paserman, Marco Daniele
Issue Date:2004
Series/Report no.:IZA Discussion paper series 996
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.
Subjects:duration data
Dirichlet process
Bayesian inference
Markov chain Monte Carlo simulation
Document Type:Working Paper
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

Files in This Item:
File Description SizeFormat
dp996.pdf523.58 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.