The Statistical Analysis of Event Histories

TLDR

Event histories are failure‑time processes where a discrete or continuous state remains constant over calendar intervals and jumps at random times, with data recording entry, exit, and subsequent state transitions. The study aims to identify factors that determine the duration spent in each state and the value of the next state entered. Continuous‑time hazard rate models are employed to analyze these determinants in event history data.

Abstract

Event histories are generated so-called failure-time processes and take this form. The dependent variable—for example, some social state—is discrete or continuous. Over time it evolves as follows: For finite periods of time (from one calendar date to another) it stays constant at a given value. At a later date, which is a random variable, the dependent variable jumps to a new value. The process evolves in this manner from the calendar date when one change occurs to a later date when another change occurs. Between the dates of the changes, the dependent variable stays constant. Data on such processes typically contain information about (a) the date a sample member entered a social state; (b) the date the state later was left, if left; (c) the value of the next state entered; and so on. In analyzing such data, the foci are on what determines the amount of time spent in each state and on what determines the value of the next state entered. This article describes how one can use continuous-time hazard rate models to address these two foci when analyzing event histories.