Multiple-Spell Regression Models for Duration Data

General models for multiple-spell duration data are considered. A general theory which indicates how the successive spells of an individual are generated by an underlying stochastic process is presented. Various special cases of the general model are discussed. The implications of different timescales are investigated: different timescales lead to different underlying stochastic processes such as Markov processes or semi-Markov processes. Occasionally common computer programs for duration data such as SAS, BMDP, GLIM and RATE can be used without further programming. Finally, multiple-spell models are applied to the duration of unemployment, and analysing the data in a single-spell framework is shown to lead to false interpretations and conclusions.