Abstract
This article presents a flexible framework for the application of stochastic processes to social science data analysis. This task becomes more and more important as large longitudinal data sets are available. We demonstrate the applicability of our methodology with an analysis of job mobility data from a cohort of young men entering the West German labor market in the early sixties. We estimate multivariate and time-dependent rate models using maximum likelihood and partial likelihood techniques to evaluate different job change patterns between upward and downward mobility. A survey of available software for event-history analysis concludes our discussion.
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