Re: st: Tests of overidentifying restrictions with -ivregress- (Out of office: Vacation )

["Newton Cheng" <ncheng@aafp.org>]

To whom it may concern,
I am out of office.
Will get back to your inqury on the week of 9/10/2012

>>> Roberto Pannico <Roberto.Pannico@uab.cat> 10/09/13 06:46 >>>

Dear all,
I need your help for interpreting some postestimation results of my instrumental variables model. I am using Stata 12.0 and the command -ivregress-. The sintax is the following:

ivregress 2sls dep (endo endoXexo = instrument1 instrument2 instrument1#exo instrument2#exo) exo exo1 exo2 exo3, first

where dep is the dependent variable, endo is the endogenous regressor, exo is an exogenous regressor that I want to interact with the endogenous one, and exo1, exo2, exo3 are other exogenous regressors. 
After running this model I type -estat overid- and I obtain this result:


Tests of overidentifying restrictions:

  Sargan (score) chi2(2) =  .311939  (p = 0.8556)
  Basmann chi2(2)        =  .310601  (p = 0.8562)


This should mean that my instruments are not correlated with the error of the main regression and therefore they are valid. Now, I want to add an other exogenous regressor in the main regression, and for this reason I write:

ivregress 2sls dep (endo endoXexo = instrument1 instrument2 instrument1#exo instrument2#exo) exo exo1 exo2 exo3 exo4, first

where exo4 is the new variable that I add to the model. The effect of this new factor on the dependent variable is statistically significant, and it also considerably  reduces the effect of endo. However, when I type again -estat overid-  the result is the following:

 Tests of overidentifying restrictions:

  Sargan (score) chi2(2) =  14.1205  (p = 0.0009)
  Basmann chi2(2)        =  14.0913  (p = 0.0009)


This means that my instruments are not valid anymore. How it can be possible? The error term of the first model should incorporate also the effect of exo4. As far as I am aware, if my instruments are not correlated to it (the error term), they can not be correlated with the error term of the second model. I don't know how to interpret these results.....
Any idea or suggestion?
Thank you very much for help
Roberto  






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