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Rose said
Tests of endogeneity of: x1 x2
H0: Regressors are exogenous
Wu-Hausman F test: 5.32266 F(2,1224) P-value
= 0.00499
Durbin-Wu-Hausman chi-sq test: 10.70872 Chi-sq(2) P-value
= 0.00473
Tests of endogeneity of: x1
H0: Regressor is exogenous
Wu-Hausman F test: 1.86877 F(1,1225) P-value
= 0.17187
Durbin-Wu-Hausman chi-sq test: 1.89181 Chi-sq(1) P-value
= 0.16900
Tests of endogeneity of: x2
H0: Regressor is exogenous
Wu-Hausman F test: 0.59057 F(1,1225) P-value
= 0.44235
Durbin-Wu-Hausman chi-sq test: 0.59848 Chi-sq(1) P-value
= 0.43916
and wants to know how these results could be obtained after estimating
the model
ivreg2 y z1--z8 (x1 x2= z9--z12)
Apparently if you ask "is x1 correlated with error" (equivalent to
using endog(x1) option on ivreg2) the evidence against the null that
it is orthogonal is weak. Likewise for x2. If you ask "can we run the
whole thing as OLS" there is sufficient evidence to reject. The result
of a joint test cannot be predicted from those of its components; you
can
reg y x1 x2 x3
with insignificant t's on x2 and x3 (particularly if their correlation
is high) but
test x2 x3
will strongly reject. Likewise here.
Kit
Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html
An Introduction to Stata Programming
| http://www.stata-press.com/books/isp.html
An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html
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