Abstract
This paper compares two approaches to analyzing longitudinal discrete-time binary outcomes. Dynamic binary response models focus on state occupancy and typically specify low-order Markovian state dependence. Multi-spell duration models focus on transitions between states and typically allow for state-specific duration dependence. We show that the former implicitly impose strong and testable restrictions on the transition probabilities. In a case study of poverty transitions, we show that these restrictions are severely rejected against the more flexible multi-spell duration models.
Highlights
This paper is about modeling discrete-time two-state panel data, where outcomes indicate which of two states an individual is occupying in each period, and where transitions between states occur between periods
Dynamic binary response (DBR) approaches focus on the probability of occupying one of the two states in each period, and usually assume Markovian state dependence, in which the current period’s occupancy depends on the occupancy of previous periods
Multi-spell duration (MSD) approaches focus on the probability of a transition between states occurring in each period, and usually assume current spell duration dependence, in which the transition probability depends on the elapsed duration in the current state
Summary
This paper is about modeling discrete-time two-state panel data, where outcomes indicate which of two states an individual is occupying in each period, and where transitions between states occur between periods. Our theoretical and empirical results suggest that data analysis can benefit from considering less restrictive models and, in particular, from considering the implications of model specifications on both probabilities of occupying a particular state and probabilities of transitioning between states. Gørgens and Hyslop (2018) showed that the DBR and MSD approaches are equivalent in a nonparametric context, and that nonparametric DBR models of order r ≤ 2 are nested within a simple MSD model Among other things, this implies that either model can be used to estimate both probabilities of occupying a state and probabilities of transitioning between states. The present paper complements that analysis by showing that the equivalence and the nesting property carries over to commonly used parametric model specifications, and by documenting that the DBR restrictions are rejected in an empirical case study.
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