Hello all, Sometimes you have an event whose outcome is only observed if another event happens. Say for example that you only observe whether someone votes Democratic if they decide to vote at all. You could think of this as a pair of probits, where the party variable is P(D=1 | V=1). In such cases, we often think that the first stage is endogenous to the second stage; that is, you're less likely to go out and vote at all if you think that your party is going to lose. This is correlated data, and there are a set of techniques for dealing with it, like bivariate probits (which I think I understand pretty well) and generalized estimating equations (which I am reading up on). Christopher Zorn from Emory has a review of this stuff in the 2001 *APSR*. He mentions that GEE models in particular "offer a number of advantages for researchers interested in modeling correlated data, including applicability to continuous, dichotomous, polychotomous, ordinal and event-count response variables." My question: I have data where the first "stage" is best modeled using an event-history framework. The second "stage" is a dichotomous response variable. I don't see duration variables listed in Zorn's lsit of applications, and so I'm wondering: is there any work on techniques for correlated data where one or more stages are duration models? Best, JP John-Paul Ferguson PhD Candidate, Economic Sociology MIT Sloan School of Management 50 Memorial Drive, E52-533 Cambridge, MA 02142 617.253.3940 (w) 617.549.8482 (c) _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l