I haven't done this sort of thing in a while, but back when I was doing it
I used discrete time methods. See Paul Allison's chapter entitled
"Discrete time methods for the analysis of event histories", pp. 61-89 in
Sociological Methodology 1982. (Edited by Samuel Leinhardt). For an
application, see "The stability of interracial friendships" by Maureen
Hallinan and Richard Williams, American Sociological Review, V. 52 Issue 5,
Oct. 1987, pp. 653-664. I believe the technique is also described in Paul
Allison's 1984 Sage book on Event History Analysis.
Basically, what you do is create a record for each time period you have
data for. Once the event occurs, you stop having data for that
individual. The dependent variable is a dichotomy (off/on) and you use
logistic regression analysis.
Like I say, I haven't done this for a while, so I don't know if this is
still state of the art, but it sounds like it could apply to your problem.
At 02:12 PM 10/19/2003 -0400, you wrote:
I am having some difficulty determining the most appropriate way to
model a process.
I have time series (month by month) data on a set of subjects, most of
whom (but not all) make a single transition from "off" to "on". My goal
is to estimate the contributions of set of time-varying and
time-invariant independent variables to the probability of this transition.
My not-so-deep understanding of survival time and hazard models suggest
that they are not what I'm looking for here. Can anybody point me in the
right direction? (Please don't spend the time on a detailed explanation;
although I'd appreciate it, I don't want to impose that much.) Thanks.
-ml
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