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Re: st: RE: control function with probit in first stage

From   "Austin Nichols" <>
Subject   Re: st: RE: control function with probit in first stage
Date   Fri, 13 Apr 2007 17:47:13 -0400

Deepak Hegde et al.--
Note that when
talks about the control function approach generalizing to nonlinear
models, I am referring to a nonlinear model in the *second* stage.
With a binary endogenous variable, you can use -ivreg2- (or even
-ivreg- I guess), or a standard control function approach, as
well--i.e. that fact that the endogenous variable is binary does not
affect consistency of the IV estimator.  The -treatreg- estimator
offers improved efficiency, however, and will show big differences in
small samples.  Another alternative appears in 18.4.1 of Wooldridge
(2002; as Procedure 18.1:

esto clear
sysuse auto, clear
treatreg pri wei, treat(for=mpg) r
esto treat
ivreg pri wei (for=mpg), r
esto ivreg
probit for wei mpg
predict ghat
ivreg pri wei (for=ghat), r
esto pr18_1
reg for wei ghat, r
predict vhat, resid
reg pri wei for vhat, r
esto pr18_1cf

Note that the SEs in col 3 are correct, col 4 not so much.  (No
correction is required for an estimated instrument, but you must
correct SEs by hand when using the control function approach.)

If I've gotten anything wrong above, I trust Mark will correct it.

On 4/13/07, Deepak Hegde <> wrote:
Wooldridge (2002 p612) has a section on this.  Imbens (p3 of the below
linked file) has a short introduction too:, as
does (p4)

The reason I am using the control function approach is because I am
explicitly interested in the coefficient/std errors on the residual (from
the first-step) in the second-stage regression.

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