5 - Models for Discrete Data

Summary

The applications of event history methods discussed to this point have all presumed that the event history process is absolutely continuous. This assumes change can occur anywhere in time. Nevertheless, continuity is often belied by the data: measures of time are frequently imprecise, or are made out of practical concerns (and out of convenience). For example, although cabinet governments may fall presumably at any time, the data used in our examples treat the termination point as occurring within a month. This implies that although we have data for processes that are continuous in nature, the data themselves are discrete. As event occurrences amass at discrete intervals, it may be more practical, and perhaps substantively natural, to consider models for discretetime processes. In this chapter, we consider some approaches for modeling event history processes where events only occur (or are only observed) at discrete intervals.

Discrete-Time Data

Event history data for discrete-time processes generally record the dependent variable as a series of binary outcomes denoting whether or not the event of interest occurred at the observation point. To illustrate, consider the public policy data in Table 5.1. These data are from a study of state adoption of restrictive abortion policy (Brace, Hall, and Langer 1999). The event of interest is whether or not a state adopted legislation that placed restrictions on abortion rights. The starting point of the analysis is the first legislative session after the Roe v. Wade decision (1973).