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From |
Yuval Arbel <yuval.arbel@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: RE: re:Re: st: Multiple endogenous regressors |

Date |
Fri, 21 Oct 2011 04:08:31 +0200 |

Several Points: 1. To summarize our previous discussion: if you solve the system by ILS and the equation is unidentified - you might get something but it will be biased and inconsistent (as I suggested). On the other hand: if you run 2SLS on unidentified equation - you get exact multicollinearity (as Kit suggested) 2. Ramanathan has no discussion at all about statistical tests related to simultaneous equation model. So you should not put the blame on him. 3. Can you apply me to the stata link of this test? it seems strange to me that the test checks whether the specification is correct. To the best of my understanding the specification of the model is based on the logic of the researcher or the economic theory, isn't it? maybe you imply that the test checks the correlation between the exogenous variable(s) and the random disturbance term - to see whether the IV is a good instrument. 3. If you look at econometrics textbooks dealing with error-in-variable models (including Greene), the only test you can find in this context is the Wu Hausman. Also, in the footnotes of table 2 of Symazki (JPE, Vol. 108 Issue 3: 590-603) the author use the term "Wu-Hausman" to describe one of the tests he carries out. 4. Finally, are you familiar with the Cox Regression and survival rates? Can you answer the question, which I sent a few hours ago? On Fri, Oct 21, 2011 at 3:03 AM, Christopher Baum <kit.baum@bc.edu> wrote: > <> > Dan said > > I agree with Kit's sentiments, but the way I read Yuval's message in (2) is that Yuval proposes that instead of estimating > > ivreg2 y (x1-x5 = z1-z5) > > Suppose I only have a 1 instrument, z, and instead propose to estimate: > > ivreg2 y (x1 = z) > ... > ivreg2 y (x5 = z) > > In this case, each model looks exactly identified, so one can get estimates (of something!). The problem here is that if the true model includes x1-x5, each model is mis-specified and includes the other 4 endogenous x's in the error term. If z is correlated with each x1-x5, then z will be correlated with the error in each of the 5 IV regression models. So, z cannot be a valid instrument for any of the 5 individual structural models. So, each of the 5 separate TSLS models will give you biased and inconsistent estimates of the include endogenous regressor. > > > > I think it was actually Elizabeth that proposed doing something like that. Naturally I agree with Dan's logic. As I said in my previous posting, if you screw up the specification of the model, then you are very likely to fail a test of overid restrictions (assuming you have some). So even if Elizabeth had two or three instruments, z1, z2, z3, and ran those five regressions above, Dan's logic would apply. > > 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/ > -- Dr. Yuval Arbel School of Business Carmel Academic Center 4 Shaar Palmer Street, Haifa, Israel e-mail: yuval.arbel@gmail.com * * 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/

**References**:**re:RE: re:Re: st: Multiple endogenous regressors***From:*Christopher Baum <kit.baum@bc.edu>

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