Thank you Mark,
Just adding this. You are right when you say that the Wald test is
missing only after a adding some parameters. The variable was not a
dummy though, but a squared variable.
Your suggestion is to drop that variable even if that variable is very
important for the analysis, right?
It might be useful to know that I think there is no need to implement
the loop you suggest. Stata suggests which parameter is causing
troubles after testing by hand all the parameters in the model using
-test-. I am not completely sure about this. Here is an example of
Stata output.
. oprobit y x1 x2 ... x34, cluster(c)
(Output omitted)
. test x1 x2...x34
(1) x1=0
(2) x2=0
.
.
.
(22)x22=0
.
.
.
(34) x34=0
Contraint 22 dropped
chi2( 33) = 396.35
Prob > chi2 = 0.0000
So, Stata automatically add "Constraint 22 dropped" before the Chi2.
Dropping manually the variable number 22 (i.e. x22) from my model I
obtained an oprobit model where the Wald test is not missing anymore.
On 9/26/06, Schaffer, Mark E <[email protected]> wrote:
Mirko,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Mirko
> Sent: Tuesday, September 26, 2006 5:38 PM
> To: [email protected]
> Subject: st: Missing Wald test with -cluster- or -robust-
> option [further explanations?]
>
> Hi,
>
> I am estimating an Ordered probit model with standard error
> adjusted for clustering using -cluster- option. Unfortunately
> the model does not provide the (adjusted) Wald test for the
> model and the relative
> Prob>Chi2.
>
> According to Stata help this could be related to the number
> of parameters and the number of clusters (i.e., the number of
> parameters must be less than the clusters).
>
> Unfortunately, this is not my case. My sample size is 1,400
> observations, I have 94 clusters and my model contains around
> 40-50 variables (most of them dummy 1/0).
>
> The same problem occurs if I use -robust- option istead of
> -cluster- or if I run -regress- (missing F and Prob>F
> missing) instead of -oprobit-.
My guess is that you have a singleton dummy (or something similar).
This causes the robust covariance matrix to be less than full rank.
You can confirm this by trying to do the Wald test by hand using -test-
or -testparm-. I suspect that you will get plausible looking test
statistics if you test most but not all of the parameters, and if you
keep adding parameters to the set you are testing, at some point the
test statistic will be missing. The last parameter you add will
correspond to the variable that causes the vcv to be less than full
rank.
Come to think of it, you could probably do this using a -foreach- loop
that loops over the variables in the estimation, and a -test- command
with the -accum- option so that you keep adding variables to the set
being tested.
Cheers,
Mark
Prof. Mark E. Schaffer
Director
Centre for Economic Reform and Transformation
Department of Economics
School of Management & Languages
Heriot-Watt University
Edinburgh EH14 4AS UK
44-131-451-3494 direct
44-131-451-3296 fax
http://www.sml.hw.ac.uk/cert
> Does anybody know why this is happening? Or at least can give
> me an alternative explanation?
>
> Thanks for your help.
>
> Mirko Moro
> University College Dublin
> *
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>
>
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