chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = -6.06 chi2<0 ==> model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test 不懂这个具体什么意思
"-help hausman- " shows that you must name the consistent estimator first (then "efficient" estimator). Therefore, if you want valid results you must specify the fixed effects estimator first (since it is the consistent estimator).
Possible solution: run fixed effects estimation first (you must name the consistent estimator first ), then run random effects regression, and then run hausman test.
Paula Garcia <paulagarciag@latinmail.com> reports getting different results from -hausman- and -xthausman-. I recommend that Paula believe the results from -hausman- and not -xthausman-. The main reason -xthausman- was undocumented was that it was too easily fooled by non positive definite (PD) differenced covariance matrices or by variables with degenerate panel behavior. Paula notes that -suest- cannot be easily run because -xtreg- does not produce scores. If Paula wants another test, I suggest an augmented regression that is asymptotically equivalent to the Hausman test (see example below). Let me take some wholesale excerpts from an earlier statalist post (I don't recall the exact day). This post addressed a substantially similar question from Eric Neumayer. ---------------------------------- Begin excerts -------------------------- Eric Neumayer <E.Neumayer@lse.ac.uk> 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 asymtotically. 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> 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. I have include below my signature a block of code that will perform an augmented regression test for Eric's model (it also performs the Hausman test using -xthause- 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 engodeniety in instrumental variables regression, and for tests of independence of irrelvant alternatives (IIA) in choice models. Regardless, the test is a staple in econometrics and it will stay in Stata. -- Vince vwiggins@stata.com Note: Paula should be able to easily adapt this code. ---------------------------------- BEGIN --- foreric.do --- 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 xthaus 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 qui test mean1 = mean1 , 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 --- foreric.do --- CUT HERE ------- * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/
sigmamore and sigmaless specify that the two covariance matrices used in the test be based on a common estimate of disturbance variance (sigma2).
sigmamore specifies that the covariance matrices be based on the estimated disturbance variance from the efficient estimator. This option provides a proper estimate of the contrast variance for so-called tests of exogeneity and overidentification in instrumental variables regression.
sigmaless specifies that the covariance matrices be based on the estimated disturbance variance from the consistent estimator.
These options can only be specified when both estimators save e(sigma) or e(rmse), or with command xtreg. e(sigma_e) is saved after command xtreg with options fe or mle. e(rmse) is saved after command xtreg with option re.
sigmamore or sigmaless are recommended when comparing fixed-effects and random-effects linear regression because they are much less likely to produce a nonpositive-definite differenced covariance matrix (although the tests are asymptotically equivalent whether or not one of the options is specified).
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