Please help!
I have been trying to proceed on this as suggested by
Justina (below), but am still stuck in what appear to
me (perhaps wrongly) to be a 'chicken-and-egg'
problem. It seems that, for regressors which one fears
to be endogenous, on the one hand one needs to find
appropriate instruments in order to test whether they
are in fact endogenous, but on the other hand one must
treat them as endogenous and select appropriate
instruments first. Is it the case that, if you suspect
a regressor might be endogenous, you should first go
through the whole process of finding an
appropriate/best instrument, and only then even test
if it is in fact endogenous?
Would the following be a correct step-by-step guide of
how to proceed, with my dependent variable ROA and
regressors a, b, c, and d, of which I theoretically
suspect d in particular of being endogenous:
1) Experiment with various possible instruments for d,
such as l.d, l4.d, and e; by running the regressions
.xtivreg2 ROA l.ROA a b c i.quarter (d=l.d) fe gmm
bw(3) robust ffirst
and repeating this using (d=l4.d) and (d=e).
2) Compare the Hansen J stats reported after the
results. A problem I have here is that the statistics
are all zero as the regressions are exactly
identified. But if I had statistics reported, would I
be looking for the largest stat as a pointer to the
most appropriate instrument?
Justina suggests looking at the F-stats of excluded
instruments, to be honest I don't see these, but there
are some F-stats in relation to the instrumentalised
variable d (pasted below). What exactly is it that I
should be comparing between regressions in order to
choose the best instrument of ones that I am
considering? [continued below output...]
===========================
Variable Partial R2 Partial R2 F(1,17962) P-value
d 0.5041 0.5041 115.76 0.0000
NB: first-stage F-stat heteroskedasticity and
autocorrelation-robust
Underidentification tests:
Chi-sq(1) P-value
Anderson canon. corr. likelihood ratio stat.
12631.56 0.0000
Cragg-Donald N*minEval stat.
18306.78 0.0000
Ho: matrix of reduced form coefficients has rank=K-1
(underidentified)
Ha: matrix has rank>=K (identified)
Weak identification statistics:
Cragg-Donald (N-L)*minEval/L2 F-stat 18256.98
NB: identification statistics not robust
Anderson-Rubin test of joint significance of
endogenous regressors B1 in main equation, Ho:B1=0
F(1,17962)= 0.24 P-val=0.6268
Chi-sq(1)= 0.24 P-val=0.6264
NB: Anderson-Rubin stat heteroskedasticity and
autocorrelation-robust
============================
3. Once i have chosen the 'best' instrument (say
l4.d), then test whether or not the suspect variable
is endogenous? By specifying
.xtivreg2 ROA l.ROA a b c i.quarter (d=l4.d) fe gmm
bw(3) robust ffirst endog(d)
And if the Ho on the endog test is rejected, this
would mean that d can in fact be treated as exogenous
after all.
Would this be a correct way of proceeding? And where I
am not sure about the endogeneity of all regressors,
how can I even find the best instrument for one in
order to test its endogenity without knowing if the
others are endogenous and their appropriate
instruments.
I am feeling really confused about this and have been
working on it and reading the relevant literature but
am struggling to operationalise it. Any simple and
practical tips would be very much appreciated.
Debbie
--- [email protected] wrote:
> Hi
>
> I am not an expert on instruments, but I suggest you
> 1) first select appropriate instruments using the
> Sargan J statistics
> and the F-stat on excl. instruments in the 1st stage
> regression (use
> ffirst option)
> 2) then test endogeneity ( Hausman Wu etc)
>
> Justina
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On
> Behalf Of Susie Enders
> Sent: 24 May 2006 20:30
> To: [email protected]
> Subject: st: -endog- test under ivreg2
>
> Hello,
>
> I have a quarterly panel dataset on which I am
> running a dynamic fixed
> effects model using the ivreg,gmm option (I have
> also tried using
> xtivreg2). I am not sure whether or not some of my
> regressors are
> endogenous and require instrumentalisation. I am
> trying to test this
> using the endog option. My confusion is that the
> outcome from the endog
> test depends on what instrument one uses for the
> suspect variable. With
> some instruments the suspect variable appears
> endogenous, with others
> not. However, not even being sure which is an
> appropriate instument, how
> do I establish whether or not the suspect variable
> is endogenous in the
> first place and hence even needing to be
> instrumentalised?
>
> (I have already read the help files as well as the
> Baum-Schaffer-Stillman paper and tried searching on
> the list archives).
>
> I have pasted below my specification and the
> relevant portion of the
> output for 3 regressions, the only difference being
> the instrument used
> to instrument the suspect variable (d) which I am
> testing whether or not
> it is endogenous. If I read the output correctly, in
> regression 1 (in
> which the instrument is the same variable lagged 4
> periods back) it
> seems that we reject Ho that the suspect variable
> (d) is exogenous, ie
> it does need to be instrumentalised. Regression 2
> (in which the
> instrument is the one-lagged variable) doesnt even
> give an output on
> endog. According to regression 3 (in which the
> instrument is another
> variable, e) it seems we fail to reject Ho so the
> suspect variable (d)
> is exogenous. How do I know whether or not it
> actually is endogenous
> without
> knowing which is the "right" intrument?
>
> A follow-up question would then be, if the suspect
> variable is indeed
> endogenous and needs to be instrumented, how I
> decide which instrument
> is best?
>
> Regression 1:
> ivreg2 ROA l.ROA a b c (d=l4.d), gmm endog(d) robust
> cl(key)
>
> Hansen J statistic (overidentification test of all
> instruments): 0.000
>
> (equation exactly identified)
> -endog- option:
> Endogeneity test of endogenous regressors:
>
> 9.550
>
> Chi-sq(1) P-val = 0.0020
> Regressors tested: d
>
------------------------------------------------------------------------
> ------
> Instrumented: d
> Included instruments: L.ROA a b c
> Excluded instruments: l4.d
> ---------------------------------
> ===============
> Regression 2:
> ivreg2 ROA l.ROA a b c (d=l.d), gmm endog(d) robust
> cl(key)
>
> Hansen J statistic (overidentification test of all
> instruments): 0.000
>
> (equation exactly identified)
>
> Collinearity/identification problems in eqn. excl.
> suspect orthog. conditions:
> C statistic not calculated for -orthog- option
>
------------------------------------------------------------------------
> ------
> Instrumented: d
> Included instruments: L.ROA a b c
> Excluded instruments: l.d
>
> ==========================================
>
> Regression 3:
>
> ivreg2 ROA l.ROA a b c (d=e), gmm endog(d) robust
> cl(key)
>
> Hansen J statistic (overidentification test of all
> instruments): 0.000
>
> (equation exactly identified)
> -endog- option:
> Endogeneity test of endogenous regressors:
>
> 0.925
>
> Chi-sq(1) P-val = 0.3362
> Regressors tested: d
>
------------------------------------------------------------------------
> ------
> Instrumented: d
> Included instruments: L.ROA a b c
> Excluded instruments: e
> ---------------------------------
>
> Thanks so much for any help with this I am so
> frustrated!
>
> Debbie
>
>
>
>
>
>
>
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