Marilyn Ibara wrote:
> I estimated a Multinominal logit model with 7 choices. I
> have 17 independent variables. I specified mlogtest after I
> ran the mlogit command.
It would have been worth mentioning the _full_ command (I suspect) you
used to get the post-estimation results shown in your post: -mlogtest,
all-.
> I am not sure about my results. Does anyone know how it is
> possible to get a negative Chi-square for the hausman test
> for IIA as I did in my results? Also, how are the degrees
> of freedom calculated, it appears that I should have 17 and
> not what is reported.
This question is answered by Long and Freese (2006: 244-5) in the second
edition of their excellent textbook, cited below my signature.
Essentially, this is very common, and it is often evidence that the
independence of irrelevant alternatives (IIA) has _not_ been violated.
They also suggest a good tip to confirm your results here: choose a
different base category and run the same -mlogtest- command.
> Furthermore, my results of the hausman test differ from the
> small-hsiao test, they contradict each other, I read online
> this is common, does anyone know how one determines which
> one is appropriate?
Long and Freese (2006: 243), quoting Monte Carlo research conducted by
Cheng and Long (2005), report that neither the Hausman-McFadden (HM) nor
the Small-Hsiao (SH) test is especially useful for assessing violations of
IIA. This is largely because both have poor size properties even with some
kinds of data, even N > 1000 in the case of HM but also sometimes for SH
when N > 500. The best advice they give is only to use -mlogit- when you
can _clearly_ distinguish between the outcome categories in your dataset.
Quite frankly, I couldn't agree more.
In sum, buy Long and Freese!
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
References:
Cheng S and Long JS (2005) "Testing For IIA in the Multinomial Logit
Model", University of Connecticut, Working Paper.
Long JS and Freese J (2nd ed, 2006) REGRESSION MODELS FOR CATEGORICAL
DEPENDENT VARIABLES USING STATA, College Station, TX: Stata Press.
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