Estimating Transition Probabilities from a Time Series of Independent Cross Sections

Abstract

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by Moffitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time‐constant and time‐varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method‐of‐scoring and how to estimate both fixed and time‐varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio‐economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.