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Re: st: Random thresholds in ordered logit/probit in -gllamm-
From
Kristian Karlson <[email protected]>
To
[email protected]
Subject
Re: st: Random thresholds in ordered logit/probit in -gllamm-
Date
Sun, 28 Nov 2010 07:27:21 +0100
Stas, thanks. I will have to reconsider the model. Identification is
without doubt weak, but I had some ideas to solve that problem. I will
have another look into that. Once again, thanks. /Kristian
Den 27-11-2010 23:57, Stas Kolenikov skrev:
I guess you would do that with -thresh()- or -ethresh()- option, but
-gllamm- only allows to use observed variables rather than random
effects. There are good reasons for that: the thresholds are collinear
with the constant (and when you estimate a model with the thresholds,
you cannot estimate the constant term; so if you try to get random
thresholds, you'd have to forego the random intercepts, as well). I
would imagine that without additional strong identification
conditions, the model would just fall apart if you had random
thresholds in the model.
I think the paper you mentioned describes essentially a -gllamm,
thresh()- model with a single random effect which essentially shifts
all thresholds together for a given individual (although you would
need to have repeated measurements for an individual to identify it).
The author may have re-written it in Mata to speed it up, but I don't
see anything in the abstract that goes beyond -gllamm-, frankly.
On Sat, Nov 27, 2010 at 3:17 AM, Kristian Karlson
<[email protected]> wrote:
Hi,
I want to estimate an ordered probit/logit model with random thresholds.
That is, the thresholds should vary across individuals. I have tried using
-gllamm-, but I fail to see how I must specify -gllamm- in order for the
thresholds to be random. I am specifically interested in this heterogeneity.
Can anyone help me on that?
In passing, I note that I came across a paper dealing with these issues,
http://econpapers.repec.org/paper/bocisug08/02.htm, but I can't identify
which command the paper refers to.
Best wishes,
Kristian
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