Bayesian Analysis of Event History Models With Unobserved Heterogeneity via Markov Chain Monte Carlo.

This article describes an interesting application of Markov chain Monte Carlo (MCMC). The authors found that MCMC worked well for estimating discrete time event history models with unobserved heterogeneity included. Although the authors found only a small amount of unobserved heterogeneity in the Iran data, it is reassuring to know that it was accounted for and that the significance of the main effects was real. There is another benefit to using MCMC to get a sample from the posterior distribution. The sample can be used to examine marginal posterior distributions of the fixed effects graphically, permitting the researcher to check for any asymmetry or nonnormality of any of the marginal distributions. This can be done using a nonparametric density estimation technique. During the investigation, the authors explored many models including other possible covariates. They fitted models using variables such as knowledge and use of various forms of contraception, duration of breast-feeding, or kind of work the woman was employed in, as well as a number of different family-planning variables. Inclusion of one or more of these variables did not improve the model, based on the Bayes factor comparing the model presented in the article with any larger model.