Re: st: RE: ivreg2 questions

[Robert Davidson <rhd773@gmail.com>]

Mark,

Sorry for what is purely an econometric question at this point
(removed from Stata) but there is still one thing that I am
misunderstanding.  In every text I can read, it basically says the
instrument must be correlated with the endogenous regressor (including
Mostly Harmless Econometrics and an Introduction to Modern
Econometrics Using Stata to name 2 - the latter stating the instrument
must be highly correlated).  These texts do not state that the
instrument must have a high correlation with the endogenous regressor
with the effect of a set of controlling variables removed (partial
correlation).  Is this just a simplification on the part of these
texts or again is there something I am missing?  And does this
basically mean that the validity of an instrument is conditional on
the other independent variables included in the primary model and not
just the dependent variable and the endogenous regressor?

Thank you again,
Rob

On Mon, Mar 19, 2012 at 7:29 PM, Schaffer, Mark E <M.E.Schaffer@hw.ac.uk> wrote:
> Rob,
>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> Robert Davidson
>> Sent: 19 March 2012 23:25
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: RE: ivreg2 questions
>>
>> Thank you for the response.
>>
>> I was looking at the Cragg Wald F-statistic, testing if the
>> equation is weakly identified, and I did check the
>> correlation with of the endogenous regressor and the
>> instrument.  I had always read that the correlation generally
>> needs to be high between the endogenous regressor and the
>> instrument in order for it to be a good instrument.
>> Is it just that the partial correlation between the two
>> should be high?
>
> Yes, exactly.
>
> --Mark
>
>>
>> Thank you,
>> Rob
>>
>> On Mon, Mar 19, 2012 at 6:53 PM, Schaffer, Mark E
>> <M.E.Schaffer@hw.ac.uk> wrote:
>> > Rob,
>> >
>> >> -----Original Message-----
>> >> From: owner-statalist@hsphsun2.harvard.edu
>> >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Robert
>> >> Davidson
>> >> Sent: 19 March 2012 21:38
>> >> To: statalist@hsphsun2.harvard.edu
>> >> Subject: st: ivreg2 questions
>> >>
>> >> Dear Statalist,
>> >>
>> >> I am using ivreg2 (3.1.03) authors cfb & mes and have a
>> few questions.
>> >>
>> >> My model is X = A + B + C (1)
>> >> The endogenous regressor, C, is binary, and I am using 1
>> continuous
>> >> instrument (D) for C.
>> >>
>> >> Q1) does this version of ivreg2 not produce the Stock-Yogo
>> relative
>> >> bias values?  My output only include the size values.
>> >
>> > It does.  However, the maximal IV relative bias test
>> requires that the estimation be sufficiently overidentified
>> (the reason is that the IV estimator has moments only up to
>> the degree of overidentification).  You've got an
>> exactly-identified model, so the relative bias test isn't
>> well defined.
>> >
>> >> Q2) I have had a difficult time finding valid instruments
>> for C.  I
>> >> have tried several that had reasonably high and significant
>> >> correlations with C and no significant correlation with the error
>> >> term from equation (1), but my f-stats were often around 7 or 8
>> >> (reasonably weak instruments).  Then, I tried another
>> variable that
>> >> was not highly correlated with C and the f-stat was about
>> 25 and the
>> >> results were similar to the main estimation.  Is it common that a
>> >> variable that does not seem strongly associated with the
>> endogenous
>> >> regressor can serve as such a strong instrument or am I doing
>> >> something wrong?
>> >
>> > This might just be a misunderstanding.  The F stat is a
>> kind of measure of partial correlation, i.e., with the
>> exogenous covariates partialled out.  Perhaps when you were
>> checking the correlations of the IVs with C, you were looking
>> at correlations instead of partial correlations?
>> >
>> > HTH,
>> > Mark
>> >
>> >> I know I cannot
>> >> test for exogeneity, but this new variable does not seem
>> correlated
>> >>with the error term from equation (1).
>> >>
>> >> I apologize that this may have been sent twice.
>> >>
>> >> Thank you,
>> >> Rob
>> >>
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