<>
Let me guess: Marian uses the default settings in her mail program that
makes her post in rich-text whereas the list only takes text-only messages.
She can change that in her options and post happily ever after. See the FAQs
whose link is attached to every message on the list...
On your prob, I have a hard time believing your first stage. Let me try to
recreate it here
*************
clear*
set obs 1000
*dependent var 70% 0 - 30% 1
g y=runiform()>0.7
*let`s see
prop y
*independent var only observed for dependent equal to 1
g x=rnormal() if y==1
*error message: "outcome does not vary"
probit y x
*************
The fact that x is observed perfectly predicts whether y equals 1. Please
advice if your estimation strategy delivers a different result.
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Tom Trikalinos
Gesendet: Donnerstag, 5. März 2009 01:44
An: [email protected]
Cc: [email protected]
Betreff: st: Probit model with randomly missing obs in the RHS: 2-stage vs
joint estimation
Sending on behalf of Marian, who has problems posting to the list --
forgive the title, it is mine, not Marian's (-- and i do not claim i
comprehend the question.)
t
-----------------------------------------------------
Marian writes:
Hi,
I need to estimate a probit with a continous variable in the RHS that
it is only observed when the binary outcome is 1. I first estimate
the continuous variable with those observations where there is no
censoring and estimate predicted values for all the observations.
(Selection is random)
Then I set the predicted value for all observations in the RHS to
estimate the probit and correct standard errors (Topel-Murphy) for
the fact I include an estimated variable in the model.
How may I estimate both equations jointly (or recursively) and
efficiently instead of in two-stages?
Thank you,
Marian
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