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st: Re: short question
Yes, you may do so. You are comparing exactly-identified IV (which by
definition has a Sargan or J of zero) with 'heteroskedastic OLS' in
that case:
. ivreg2 price (mpg = headroom )
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only
Number of obs
= 74
F( 1, 72)
= 1.16
Prob > F
= 0.2861
Total (centered) SS = 635065396.1 Centered R2
= 0.1828
Total (uncentered) SS = 3447834321 Uncentered R2
= 0.8495
Residual SS = 518998088.2 Root MSE
= 2648
------------------------------------------------------------------------------
price | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
mpg | -141.0716 129.4705 -1.09 0.276 -394.8291
112.6859
_cons | 9169.701 2774.505 3.30 0.001 3731.772
14607.63
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM
statistic): 12.671
Chi-sq(1) P-val
= 0.0004
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F
statistic): 14.876
Stock-Yogo weak ID test critical values: 10% maximal IV
size 16.38
15% maximal IV
size 8.96
20% maximal IV
size 6.66
25% maximal IV
size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all
instruments): 0.000
(equation exactly
identified)
------------------------------------------------------------------------------
Instrumented: mpg
Excluded instruments: headroom
------------------------------------------------------------------------------
. ivreg2 price (mpg = headroom ), endog(mpg)
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only
Number of obs
= 74
F( 1, 72)
= 1.16
Prob > F
= 0.2861
Total (centered) SS = 635065396.1 Centered R2
= 0.1828
Total (uncentered) SS = 3447834321 Uncentered R2
= 0.8495
Residual SS = 518998088.2 Root MSE
= 2648
------------------------------------------------------------------------------
price | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
mpg | -141.0716 129.4705 -1.09 0.276 -394.8291
112.6859
_cons | 9169.701 2774.505 3.30 0.001 3731.772
14607.63
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM
statistic): 12.671
Chi-sq(1) P-val
= 0.0004
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F
statistic): 14.876
Stock-Yogo weak ID test critical values: 10% maximal IV
size 16.38
15% maximal IV
size 8.96
20% maximal IV
size 6.66
25% maximal IV
size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all
instruments): 0.000
(equation exactly
identified)
-endog- option:
Endogeneity test of endogenous
regressors: 0.721
Chi-sq(1) P-val
= 0.3957
Regressors tested: mpg
------------------------------------------------------------------------------
Instrumented: mpg
Excluded instruments: headroom
------------------------------------------------------------------------------
In this case the large p-val shows that the null hypothesis that the
endogenous regressors are orthogonal to the error term cannot be
rejected, and IV estimation is not required.
You could reach the same conclusion with -ivendog- after the original
estimation:
. ivendog
Tests of endogeneity of: mpg
H0: Regressor is exogenous
Wu-Hausman F test: 0.69889 F(1,71) P-value
= 0.40596
Durbin-Wu-Hausman chi-sq test: 0.72132 Chi-sq(1) P-value
= 0.39571
Cheers
Kit
Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html
On Aug 18, 2008, at 13:04 , Emiliano Sironi wrote:
Dear professor,
I'm a Ph.D. student from bocconi university (milan, italy) and I've
just written to you few months ago to ask about your routine ivreg2
in stata. I'm writing a paper on a topic of law and economics and I
bought your book "An introduction to the modern econometrics using
stata". In page 211-213 (chapter 8.11) you described tests for
endogeneity of regressors. The main topic illustrated in the chapter
is the Durbin-Wu-Hausman test, but this test is not allowed for
clustered or p-weighted data, according to Stata Program instructions.
In the chapter and in your paper on Stata Press (with Schaffer and
Stillman), you present, as an alternative, the C statistic by
Hansen, Sargan, Basmann which is perfectly available in my case.
The question is...in my model I have only one endogenous variable
(x) with only one excluded instrument (z). Can I use the C statistic
in order to test the endogeneity of x? The approach would be similar
to that used at page 213, but in your example the model is
overidentified.
Sorry if I've written in August, but your kindness in answering to
the reasercher's questions is known among my collegues!
Best wishes,
Emiliano
--
Emiliano Sironi
Dipartimento di Scienze delle Decisioni
Universit� "L. Bocconi"
Via Roentgen, 1 - Piano 3� - Stanza D1-05
20135 Milano
Tel: +39 02 5836 5365
e-mail: [email protected]
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