I have also not done the algebra, but I sincerely doubt that the
example Tobias coded, where one excluded instrument is used to
identify the effects of two endogenous dummy variables, can possibly
be right.
A model in which the endogenous regressor is an ordered categorical
variable and the dependent variable is continuous can be fitted using
-ivreg-, since its consistency does not depend on the endog var having
a particular distribution, and trading a tiny efficiency gain for a
well-understood estimator, with no known errors in the code, is well
worth it, IMHO.
See -help _robust- and [P] _robust for help on robust var and clusters.
On 3/15/06, Brian P. Poi <[email protected]> wrote:
> (message trimmed)
In your program, the first stage is fit via -oprobit- and the second stage
via -regress-, which implies to me that you are envisioning a model in
which the endogenous regressor is an ordered categorical variable and the
dependent variable is continuous.
If you are interested in a model like -ivprobit- with an ordered dependent
variable, then the two-step estimator of Rivers and Vuong for probit
(1988, Journal of Econometrics) could probably be extended in a
straightforward way. Newey's efficient estimator (1987, Journal of
Econometrics) might also be a viable option, though it would a bit more
work to code, since it makes use of a two-step estimator like Rivers and
Voung's. The maximum likelihood estimator as used by -ivprobit- could
also be generalized. (These ideas should be taken as conjecture -- in
principle they should work, though I haven't done the algebra to guarantee
that they will work or are practical to implement.)
If, on the other hand, you mean a model where the endogenous regressor is
an ordered categorical variable, then I don't have anything to add, other
than a guess that the treatment effects literature may have something to
say.
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