It is my understanding that the mvprobit command does not require one to
have the same exact set of regressors for every equation. If so, why not
drop the variable that is predicting perfectly from the equation(s) for
which it makes perfect predictions, and leave it in the equation(s) for
which it does *not* make perfect predictions?
HTH
Sam
On Mon, 19 Nov 2007, Georg Metzger wrote:
> Dear all,
> I have a problem in estimating a multivariate probit ('mvprobit' from
> ssc).
> I want to estimate a multivariate probit model with four equations.
> There is an (important) variable in the specification, which predicts
> the outcome of the fourth equation perfectly. In a normal probit
> estimation both this variable and the relevant observations would be
> dropped out. However, it is not the case if applying the mvprobit
> command, I think, because the variable and the relevant observations
> contribute to explain the outcome of the other equations. The mvprobit
> estimation results in outrageous values for both the coefficient and the
> standard error of this variable. I know that one can't interpret this
> values; however, does anybody know if including the variable
> significantly bias the other estimation results? Or should I better
> leave out the equation with the perfect fit estimating a trivariate
> specification?
>
> Best,
> Georg
>
> Georg Metzger
> Zentrum f�r Europ�ische Wirtschaftsforschung (ZEW) GmbH
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> Zentrum f�r Europ�ische Wirtschaftsforschung GmbH (ZEW)
> Centre for European Economic Research
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>
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>
>
> Zentrum f�r Europ�ische Wirtschaftsforschung GmbH (ZEW)
> Centre for European Economic Research
> L 7, 1 � 68161 Mannheim � Germany
> Sitz der Gesellschaft: Mannheim � Amtsgericht Mannheim HRB 6554
> Aufsichtsratsvorsitzender: Finanzminister Gerhard Stratthaus MdL
> Geschaeftsfuehrer: Prof. Dr. Dr. h.c. mult. Wolfgang Franz, Thomas Kohl
> ------------------------------------------------------------------------
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