On Fri, 30 Oct 2020, Riccardo (Jack) Lucchetti wrote:
The LR test is the traditional way of checking the independence
hypothesis,
but I have no idea if it's statistically tenable in the context of QML
estimation. On the other hand, its is relatively simple to set up a (pseudo)
Wald test for the same hypothesis. I'll have a look at this.
Hm, this will require some more work than I had thought. In fact, I'm not
quite sure we're doing the right thing in the current incarnation of
biprobit. Let me explain. The number of estimated coefficients is k1 + k2
+ 1, where k1 and k2 are the numbers of explanatory variables in the first
and second equation respectively, plus there's rho (actually, its
hypherbolic arctangent).
When we record $vcv into the MODEL struct, we treat the model as if the
number of parameters were k1 + k2, although the covariance matrix is 1
element bigger, with the result that
(a) the $vcv you now get after biprobit is buggy (it's not symmetric)
(b) we don't have in our MODEL struct the estimated variance for
atan(rho).
Note that we treat $coeff in the same way, that is we don't store rho
among the estimated parameters, although its estimate is accessible as
$rho or $model.rho.
So I'm wondering: while we're at it, should we "correct" this, at the
risk
of backward incompatibility, by including rho in $coeff and making $vcv a
square matrix of dimension (k1 + k2 + 1), or shall we just fix $vcv and
maybe provide an accessor for the standard error of rho?
IMO we should change the output of biprobit and make rho a "first class
citizen", that is include it in the output just like the other estimated
parameters as well as storing it as the last element of $coeff and
enlarging $vcv and $stderr as needed, but I'd like to hear your opinion on
this before I start working on the code.
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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