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Re: st: Competing Risk for repeated event nominal dependent variables

From   Mike Lacy <>
Subject   Re: st: Competing Risk for repeated event nominal dependent variables
Date   Tue, 02 Mar 2010 11:44:11 -0700

>Date: Sun, 28 Feb 2010 11:55:44 -0500
>From: "David A. Cort" <>
>Subject: st: Competing Risk for repeated event nominal dependent variables
>Dear Listserv,
>I am attempting to fit discrete-time event history model where the
>outcome is nominal and can be repeated over time. The social process is
>residential mobility. Instead of wanting to know the risk of moving into
>a community or neighborhood (Allison-type model), I'm interested in the
>risk of moving into a specific type of neighborhood. The dependent
>variable therefore has multiple categories (4 to be specific) for
>neighborhood type and time is discretized (into months). Any help
>concerning how STATA 10 can handle this type of setup would be very helpful.

I have looked for the same kind of thing, and have not run across any settled "practical" advice, so others' views here would be most welcome (i.e., needed). That being said, here's what I have come across up to this point.

1) Converting to a person-period file, with k outcomes for each case, and then using multinomial logit with "stay" as the base category.

2) The problem with 1) that it will almost certainly fail the IIA test. One approach is outlined in Hill D H; Axinn W G; Thornton A. 1993. "Competing hazards with shared unmeasured risk factors."
Sociological Methodology 23:245-77.
What they suggest, as best I understand it, amounts to a nested logit model, with (in your case), all the "move states" in one nest, and "stay" in the other. The first stage is move vs. stay, and the second stage is "which move," given "move." When I tried something like this, I ran into issues with wanting/needing to use some of the same variables as predictors at both states (e.g., income influences the decision to move at all, and given the choice to move, it influences the kind of move), which -nlogit- seemed not to tolerate.

3) I have seen approaches using multinomial logit with a random intercept. I don't know how good a solution this is. In principle, the model should be estimable with -gllamm- or -mixlogit-, though perhaps not easily with a file as large as one is likely to get after conversion into person-period format. Perhaps someone can comment from on the feasibility as well as desirability of this approach.

4) I recently saw some suggestions (can't find them at the moment) to use multinomial logit, with a "discrete factor" rather than random intercept. (I can't find a published source. I found an online Rand working paper by Z. Nazarov at . Although the application is different, the sense of "discrete factor" here is as in Mroz, T. A. 1999. "Discrete factor approximations in simultaneous equation models:Estimating the impact of a dummy endogenous variable on a continuous outcome." Journal of Econometrics, 92, 233-274.

This seems an interesting approach, and I would be particularly interested if anyone has ideas or pointers to "discrete factor" analyses using Stata.

Mike Lacy, Dept. of Sociology, Colorado State University, Fort Collins, CO 80523

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