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dc.contributor.authorPaserman, Marco Danieleen_US
dc.description.abstractThis 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.en_US
dc.publisherInstitute for the Study of Labor (IZA) Bonn-
dc.relation.ispartofseriesIZA Discussion paper series 996en_US
dc.subject.keywordduration dataen_US
dc.subject.keywordDirichlet processen_US
dc.subject.keywordBayesian inferenceen_US
dc.subject.keywordMarkov chain Monte Carlo simulationen_US
dc.subject.stwStatistische Bestandsanalyseen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwVereinigte Staatenen_US
dc.titleBayesian inference for duration data with unobserved and unknown heterogeneity : Monte Carlo evidence and an applicationen_US
dc.typeWorking Paperen_US
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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