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st: biprobit gof test, partial observability, survey data (svy)
From
J Gonzalez <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: biprobit gof test, partial observability, survey data (svy)
Date
Wed, 5 Oct 2011 01:35:08 +0100 (BST)
Hello stata list members
I am working on a bivariate probit model (biprobit) with partial observability that looks like this.
y1 = x1 + x2 + x3 + u
y2 = x1 + x2 + v
But I only have available y=y1*y2 (hence, there is partial observability).
And I am fitting the model with survey data (using svyset, with pweight and strata), so I run something like:
svy:biprobit (y1po = x1 x2 x3) (y2po = x1 x2), partial
where y1po=y2po=y=y1*y2
However, I do not have clear how to test this model (gof test, normality assumption) and how to decide among competing specifications.
I have been reading (Cameron & Trivedi, 2005; Maddala, 1983) and also the stata list archives, and althought I have indeed found several tests I am still not sure how to proceed because of the svy complication and the partial observability issue.
I have found the likelihood ratio testof rho=0, the biprobit command usually estimates, but according to the stata manual likelihood ratio tests is not appropriate with svy, so I cannot use it for my model (and actually the svy:biprobit does not produce it).
I also found Murphy's score test (*), but according to the help file the test does not work when the partial option is used, hence, I neither can use this test.
Similarly, the estat ic command for the information criteria is also not suitable after svy estimation.
So, which tests may and should I use?
How to test if rho=0?
Which criteria may I use to decide among competing specifications?
Thanks for your help.
Jesus González
* https://webspace.utexas.edu/rcc485/www/papers/murphycomment.pdf
* https://webspace.utexas.edu/rcc485/www/code.html
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics. Methods and Applications. New York: Cambridge University Press.
Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics (Vol. 3): Cambridge Univ Press.
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