Fitting Event-History Models to Uneventful Data

Douglas A. Wolf () and Thomas M. Gill ()
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Douglas A. Wolf: Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020, https://www.maxwell.syr.edu/directory/douglas-a-wolf
Thomas M. Gill: Yale University School of Medicine; Dorothy Adler Geriatric Assessment Center, New Haven, CT 08504

No 101, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: Data with which to study disability dynamics usually take the form of successive current-status measures of disability rather than a record of events or spell durations. One recent paper presented a semi-Markov model of disability dynamics in which spell durations were inferred from sequences of current-status measures taken at 12-month intervals. In that analysis, it was assumed that no unobserved disablement transitions occurred between annual interviews. We use data from a longitudinal survey in which participants' disability was measured at monthy intervals, and simulate the survival curves for remaining disabled that would be obtained with 1- and 12-month follow-up intervals. The median length of an episode of disability based on the 12-month interval data is over 22 months, while the "true" median, based on the 1-month interval data, is only one month.

Keywords: Disability; semi-Markov process; duration analysis (search for similar items in EconPapers)
JEL-codes: C41 C81 I19 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2008-01
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:101

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