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From |
Ari Dothan <ari.dothan@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: -endog-test under xtivreg2: Jansen's J-stats |

Date |
Wed, 23 Feb 2011 16:23:36 +0200 |

Thanks a lot, Professor Baum. I have not seen these considerations elsewhere Best regards On 2/23/11, Christopher Baum <kit.baum@bc.edu> wrote: > <> > " > "If it's overidentified, i.e., you're using all the plausible > instruments (see my comment above), then the J stat is a specification > test. If the stat is large, then you *fail* the test, because the > null is that all the instruments are valid, and a large J stat means > you reject the null." > > can someone explain why a large J means rejection of the null in this case? > > > The Hansen (not Jansen!) J statistic is the GMM optimization criterion. In > an exactly ID model, you can always make it zero because you have as > many equations as unknowns (parameters) and in general that can be solved > exactly. In an overID model, you have more equations (moment conditions) > than parameters, so for any choice of parameters, it is likely that you will > fail to satisfy some (or all) of the moment conditions exactly. But remember > that > the moment conditions are of the form E[Z'u] = 0, so that they are asserting > that each column of the instrument matrix is orthogonal to the error term. > If the data > disagree violently with one or more of those conditions, you have evidence > that at least some of the instruments are not properly exogenous. That > disagreement manifests itself in a large J, that is, you tried to minimize > something and it ended up pretty big. > > The (joint) null is that the model is specified properly (y = X b+ u in the > case of IV-GMM) AND E[Z'u] = 0. If you get a large J, there is evidence > against that > null, so you reject the hypothesis that your instruments are exogenous > AND/OR that the equation is properly specified. > > We have an example somewhere (perhaps in one of the B-S-S papers) where you > get a huge J, which goes away if you move an excluded instrument > into the equation. That is a case where the J is challenging specification > rather than exogeneity (the instrument in question cannot be correlated with > error by construction, as it is something like age). > > 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 > > > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- Ari Dothan * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: -endog-test under xtivreg2: Jansen's J-stats***From:*DE SOUZA Eric <eric.de_souza@coleurope.eu>

**References**:**re: st: -endog-test under xtivreg2: Jansen's J-stats***From:*Christopher Baum <kit.baum@bc.edu>

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