Dynamic discrete-time duration models. (REVISED)

Discrete-time grouped duration data, with one or multiple types of
terminating events, are often observed in social sciences or
economics. In this paper we suggest and discuss dynamic models for
flexible Bayesian nonparametric analysis of such data. These models
allow simultaneous incorporation and estimation of baseline hazards
and time-varying covariate effects, without imposing particular
parametric forms. Methods for exploring the possibility of
time-varying effects, as for example the impact of nationality or
unemployment insurance benefits on the probability of
re-employment, have recently gained increasing interest. Our
modelling and estimation approach is fully Bayesian and makes use
of Markov Chain Monte Carlo (MCMC) simulation techniques. A
detailed analysis of unemployment duration data, with full-time
job, part-time job and other causes as terminating events,
illustrates our methods and shows how they can be used to obtain
refined results and interpretations.