All,
OK, yet another question I wished to ask the list that I'll probably get
flamed for, but I think it's interesting (and essential for me to fix).
I've recently fitted some -xtprobit- models quite recently. It took time,
but bar one exception, all the models ran with decent results. As a new
Statabod, I was rather pleased with myself (not that that's ever too
difficult). Then I realised something: since this was panel survey data I
was using, I didn't weight the analysis!! :((
I had to start over trying to use Stata's weights. Unfortunately, it won't
run [pw=...], [aw=...] or [fw=...]. It ran [iw=...), but it returned a
model full of low coefficents and highly insignificant p-values. Given
that the data was sampled from households at random, [aw] and [fw] do not
appear
to be appropriate (certainly going by their descriptions in [U], 23.16.
Option [pw] looks to be the most appropriate, but Stata returns the error:
"pweight not allowed in random-effects case r(101);".
It's been suggested to me that, instead, I may be able to reduce the
effect by entering the number of adults in household, age and marital
status and other such variables in the model. This is because Scottish
households were oversampled and that people in small households have a
much larger chance of selection than people in large households in this
British survey (e.g. a person in a one adult household has twice the
selection chance of a person in a two adult household). I've entered the
last two of these in my models, but not the first (yet).
So to bring this mini-epic to its cliffhanger, could adding such variables
get round the problem of not being able to formally use -[pw=(weight)]- in
-xtprobit-? Or are there other automatic fixes?
Thanks in advance. :)
Yours,
CLIVE NICHOLAS,
Politics Building,
School of Geography, Politics and Sociology,
University of Newcastle-upon-Tyne,
Newcastle-upon-Tyne,
NE1 7RU,
United Kingdom.
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