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From |
John Antonakis <John.Antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Hausman test (via a Wald test) |

Date |
Sun, 01 May 2011 20:45:17 +0200 |

Agreed--but this is not what I am getting it.

Best, J. __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 01.05.2011 19:01, Brian P. Poi wrote:

On 05/01/2011 12:01 PM, John Antonakis wrote:Right, Eric. But some estimators are more efficient; take the case where you compare IV estimates (consistent) versus OLS estimates (efficient). The Hausman test will suggest that an efficient estimator is not consistent in the case where, for example, a regressor is endogenous, because the consistent vs efficient estimates will be significantly different, as tested by the usual Hausman test: (d_IV - d_OLS)/(SE_d^2_IV - SE_d^2_OLS)^-1. Thus, I am suggesting that one uses the Wald postestimation test following the OLS model to determine whether the estimate/s of the OLS model is/are significantly different from the estimate/s of the IV model. My question has to do with the fact that the Wald test, in this case, tests the OLS coefficient/s against a specific value/s (that of the IV estimator) and ignores the variance in the IV estimate/s--so I was wondering whether this was econometrically sound to do.In the case of IV versus OLS, there is a way to use a Wald test afterOLS to see whether the IV and OLS estimates are significantlydifferent, though it doesn't escape the need to have instrumentsavailable in the first place. In this case, fit the regressiony1 = y2*beta + X1*gamma + V*theta + errorby OLS where y2 is your endogenous regressor(s), X1 are exogenousvariables, and V are the residuals from the regression(s)y2 = X1*pi1 + x2*pi2 + errorwith extra instruments X2. Test whether theta = 0 using a Wald test;a rejection of H0 suggests that y2 is endogenous and therefore thatOLS is not consistent.But to reiterate, this presumes you are able to come up withinstruments in the first place so that you can add that V term to yourOLS regression.>> I was wondering, though, if anyone is aware of literature showing that >> a Wald test could do too (which would be particularly useful in the >> case of models where the Hausman test can't be used in Stata); more >> specifically, the Wald test I am suggesting is to test whether the >> parameters (of interest) from the efficient estimator are >> significantly different from those of the consistent estimator. ItThere's also a way to do a Wald test equivalent to a Hausman test offixed versus random effects. Instead of doing. xtreg y x1 x2, re do . egen x1bar = mean(x1), by(panelvar) . egen x2bar = mean(x2), by(panelvar) . xtreg y x1 x2 x1bar x2bar, re . test x1bar x2bar Wooldridge derives this test in his cross-sectional and panel textbook.There are probably other examples of Hausman-like tests that can bedone as Wald tests, too.-- Brian Poi -- brian@poiholdings.comBest, J. __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 01.05.2011 17:03, DE SOUZA Eric wrote:John, I don't get your point. Consistency refers to the parameter estimate, efficiency refers to the variance of the estimate. The Hausman principle compares two estimators, both of which are consistent under the null but only one under the alternative. This is quite general. Eric de Souza College of Europe Brugge (Bruges), Belgium http://www.coleurope.eu -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of JohnAntonakisSent: 01 May 2011 16:51 To: statalist@hsphsun2.harvard.edu Subject: st: Hausman test (via a Wald test) Hi: Given that sometimes Stata cannot compute the Hausman test (because it is undefined or because the estimation procedure does not allow it), I have been thinking about ways to go around this limitation (and find SUEST to be particularly useful in this regard). I was wondering, though, if anyone is aware of literature showing that a Wald test could do too (which would be particularly useful in the case of models where the Hausman test can't be used in Stata); more specifically, the Wald test I am suggesting is to test whether the parameters (of interest) from the efficient estimator are significantly different from those of the consistent estimator. It would be very simply to do and could accommodate a large class of estimators. However, this test is only a constraint on the coefficient estimate/s from the efficient estimator and ignores the variance of estimates from the consistent estimator (thus I don't know to what extent this test would be useful). Any thoughts? Best, John. -- __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________* * 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/

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**References**:**st: Hausman test (via a Wald test)***From:*John Antonakis <John.Antonakis@unil.ch>

**st: RE: Hausman test (via a Wald test)***From:*DE SOUZA Eric <eric.de_souza@coleurope.eu>

**Re: st: RE: Hausman test (via a Wald test)***From:*John Antonakis <John.Antonakis@unil.ch>

**Re: st: RE: Hausman test (via a Wald test)***From:*"Brian P. Poi" <brian@poiholdings.com>

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