Wednesday, August 24, 2005

Re: st: hausman and xthausman after panel fe, re

Hi Vince, I have a similar problem. I obtain this output after using Hausman. Since, suest is unavailable after xtreg, can I simply go ahead and present the results from xtreg, re?

. hausman fixed random

---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------- age | -6519.588 -6822.915 303.3273 . v | -411.6388 -31331.31 30919.67 . totlsa | 3393.901 3503.643 -109.7421 . numcicd | -61109.29 -63309.05 2199.759 . ------------------------------------------------------------------------------ b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic

chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B) = -6.22 chi2<0 ==> model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test

Best, Alice

>From: (Vince Wiggins, StataCorp) >Reply-To: >To: >Subject: Re: st: hausman and xthausman after panel fe, re >Date: Tue, 23 Aug 2005 10:17:26 -0500 > >Carl Nelson <> asks why he gets different results >from >the -hausman- command and the deprecated -xthausman- command. > > > This question concerns problem 10.9 in Jeff Wooldridge's book > > Econometric Analysis of Cross Section and Panel Data. In this > > exercise, which I gave to some students in a course this summer, > > using Cornwell.dat students are asked to estimate xtreg, fe and > > xtreg, re and perform the hausman test. Using the old xthausman > > syntax the result is a significant test statistic (approximately 121 > > for a chisquared(11) rv). Using the newer hausman syntax the result > > is a negative chisquared statistic and warning about violation of > > assumptions. I constructed the statistic from the saved results > > e(b) and e(V) and I got the same result as the newer hausman syntax. > > [...] > >It is rare that -hausman- and -xthausman- produce different statistics, but >I >recommend that Carl believe the results from -hausman- and not -xthausman-. >The main reason -xthausman- was undocumented (and now works only under >version >control) was that that it could be fooled by non positive definite (PD) >differenced covariance matrices or by variables with degenerate panel >behavior. > >I posted a rather lengthy discussion of the issues back in March of 2002. >This post predates some of the statalist archives, so at the risk of being >long-winded yet again, let me quote from that posting. > >---------------------------------- Begin excerpts >-------------------------- > >Eric Neumayer <> asks why he is getting different >results >from -xthaus- and -hausman- when testing for fixed vs. random effects after >estimation with -xtreg-. [...] > >I believe there are open questions about Hausman tests in situations like >Eric's, see the explanation that follows. > > >Preliminaries >------------- > >It is hard to discuss the Hausman test without being specific about how the >test is performed. Let B be the parameter estimates from a fully efficient >estimator (random-effects regression in this case) and b be the estimates >from >a less efficient estimator (fixed-effects regression), but one that is >consistent in the face of one or more violated assumptions, in this case >that >the effects are correlated with one or more of the regressors. If the >assumption is violated then we expect that the estimates from the two >estimators will not be the same, b~=B. > >The Hausman test is essentially a Wald test that (b-B)==0 for all >coefficients >where the covariance matrix for b-B is taken as the difference of the >covariance matrices (VCEs) for b and B. What is amazing about the test is >that we can just subtract these two covariance matrices to get an estimate >of >the covariance matrix of (b-B) without even considering that the VCEs of >the >two estimators might be correlated -- they are after all estimated on the >same >data. We can just subtract, but only because the the VCE of the fully >efficient estimator is uncorrelated with the VCEs of all other estimators, >see >Hausman and Taylor (1981), "panel data and unobservable individual >effects", >econometrica, 49, 1337-1398). The VCE of the efficient estimator will also >be >smaller than the less efficient estimator. Taken together, these results >imply that the subtraction of the two VCE (V_b-V_B) will be positive >definite >(PD) and that we need not consider the covariance between the two VCEs. > >These results, however, hold only asymptotically. For any given finite >sample >we have no reason to believe that (V_b-V_B) will be PD. So, it is amazing >that we can just subtract these two matrices, but the price we pay is that >we >can only do so safely if we have an infinite amount of data. The Hausman >test, unlike most tests, relies on asymptotic arguments not only for its >distribution, but for its ability to be computed! Let's discuss what we do >what we do when (V_b-V_B) in not PD in the context of Eric's results. > >Aside: If anyone is interested in a Hausman-like test that drops the >assumption that either estimator is fully efficient, actually estimates the >covariance between the VCEs, and can always be computed, see Weesie (2000) >"Seemingly unrelated est. and cluster-adjusted sandwich estimator", STB >Reprints Vol 9, pp 231-248. The test unfortunately requires the scores >from >the estimator, and -xtreg, fe- does not directly produce these. > ><Note, a version of -suest- command is now official, but is still >unavailable > after -xtreg-> > > >Of Inverses and Hausman Statistics >---------------------------------- > >The reason that -xthaus- and -hausman- produce different statistics on >Eric's >models is that they take different inverses of this non-PD matrix. >-xthaus- >uses Stata's -syminv()- which zeros out columns and rows to form a >sub-matrix >that is PD and inverts that matrix, whereas -hausman- uses a Moore-Penrose >generalized inverse. Most of the literature on Hausman tests suggests that >a >generalized inverse such as Moore-Penrose be used when the matrix is not >PD, >however, I have not seen a foundation of this suggestion (and would >appreciation a reference if anyone knows of one). > >Two of us at Stata have independently run some informal simulations, where >non-PD matrices are common, to determine if either of these inverses has >nominal coverage for a true null. While these simulations are not complete >enough to share or publish, we both found that neither inverse performs >well. >This doesn't seem too surprising to me, if the information in our sample is >insufficient to produce a PD "VCE" then the basis of the test would seem to >be >in question. > >-xthaus- does not make it clear when the matrix is not PD. I recall having >read, though I cannot now find the reference, that in the case of random >vs. >fixed effects that the matrix was either always PD. This may have been the >thinking in excluding this check from -xthausman-. Regardless, it is >clearly >not impossible and is not even unlikely. Simulations show that non-PD >matrices are quite common. > > >An Alternative >-------------- > >Even in their early work, Hausman and Taylor (1981) discuss an >asymptotically >equivalent test for random vs. fixed effects using an augmented regression. >There are actually several forms of the augmented regression, all of which >are >asymptotically equivalent to the Hausman test. All of these augmented >regression tests are based on estimating an augmented regression that nests >both the random- and fixed-effects models. They are parameterized in such >a >way that we can perform a simple Wald test of a set of the jointly >estimated >coefficients. They have fewer of the mechanical and interpretation >problems >associated with the Hausman test. Their results will differ numerically >from >the Hausman test in finite samples because they are only asymptotically >equivalent. > >I have include below a block of code that will perform an augmented >regression >test for Eric's model (it also performs the Hausman test using -xthaus- and >-hausman-). It can easily be adapted to any model by changing the depvar >and >varlist macros. > >If I have given the impression that I don't much care for the Hausman test, >good. I don't. In ad hoc simulations I have found that in addition to its >proclivity to be uncomputable, the test has low power for the current >problem, >for tests of endogeneity in instrumental variables regression, and for >tests >of independence of irrelevant alternatives (IIA) in choice models. > >Regardless, the test is a staple in econometrics and it will stay in Stata. > > ><Note: Carl should be able to easily adapt this code by specifying the id > variable, dependent variable, and varlist.> > >---------------------------------- BEGIN --- --- CUT HERE >------- >local id myid >local depvar lnuncs >local varlist lngdp ecrise ecfall urban lnhouse femalepa male1544 /* > */ lndiscr lnfree lnpts latin ssa deathp rulelaw protest cathol /* > */ muslim transiti lnethv oecd war year89 year92 year95 > >xtreg `depvar' `varlist', re >hausman, save >version 7: xthausman > >xtreg `depvar' `varlist', fe >hausman, less > >tokenize `varlist' >local i 1 >while "``i''" != "" { > qui by `id': gen double mean`i' = sum(``i'') / _n > qui by `id': replace mean`i' = mean`i'[_N] > qui by `id': gen double diff`i' = ``i'' - mean`i' > local newlist `newlist' mean`i' diff`i' > > local i = `i' + 1 >} > >xtreg `depvar' `newlist' , re >tempname b >matrix `b' = e(b) > >qui test mean1 = diff1 , notest /* clear test */ >local i 2 >while "``i''" != "" { > if `b'[1,colnumb(`b', "mean`i'")] != 0 & /* > */ `b'[1,colnumb(`b', "diff`i'")] != 0 { > qui test mean`i' = diff`i' , accum notest > } > local i = `i' + 1 >} >test > >---------------------------------- END --- --- CUT HERE >------- > >---------------------------------- End excerpts >-------------------------- > >As noted in the excerpt, When -xthausman- was written we were swayed by >published "proofs" that the difference matrix was required mathematically >to >be positive definite when comparing FE and RE linear regression. As Eric's >and Carl's examples show, this is not true. I would like to thank Mark >Schaffer <> for reminding me of one of the "proofs", > > > "This appendix proves that the Avar(q_hat) in (5.2.21) is > positive definite and the Hausman statistic (5.2.22) is > guaranteed to be nonnegative in any finite samples." > > Hayashi, Econometrics (2000), Appendix 5.A, pp. 346-349 and >334-335. > >To avoid breaking user's do-files, we were reluctant to remove -xthausman- >when -hausman- was first introduced. Sufficient time has passed, and as of >version 9 of Stata, -xthausman- works only when your version is set to 8 or >lower. > > >-- Vince > > >* >* For searches and help try: >* >* >*

_________________________________________________________________ Express yourself instantly with MSN Messenger! Download today - it's FREE!

* * For searches and help try: * * *


Links to this post:

Create a Link

<< Home

This page is powered by Blogger. Isn't yours?