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| From | John Antonakis <John.Antonakis@unil.ch> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: fixed effect or random effect model |
| Date | Sun, 06 May 2012 10:31:21 +0200 |
In your case, it would be best to use the user-written -xtoverid- test (available from SSC) after having run
xtreg cost duration sex age group, re cluster(id_indicator) (id_indicator is your panel identifier)The xtoverid test accommodates a cluster robust xtreg vce. Specifically, it is a Hausman-type test that constrains the covariance between uj (the fixed-effect) and the regressors to zero. See "help xtoverid": here is the relevant extract from the help file:
"A test of fixed vs. random effects can also be seen as a test of overidentifying restrictions. The fixed effects estimator uses the orthogonality conditions that the regressors are uncorrelated with the idiosyncratic error e_it, i.e., E(X_it*e_it)=0. The random effects estimator uses the additional orthogonality conditions that the regressors are uncorrelated with the group-specific error u_i (the "random effect"), i.e., E(X_it*u_i)=0. These additional orthogonality conditions are overidentifying restrictions. The test is implemented by xtoverid using the artificial regression approach described by Arellano (1993) and Wooldridge (2002, pp. 290-91), in which a random effects equation is reestimated augmented with additional variables consisting of the original regressors transformed into deviations-from-mean form. The test statistic is a Wald test of the significance of these additional regressors. A large-sample chi-squared test statistic is reported with no degrees-of-freedom corrections. Under conditional homoskedasticity, this test statistic is asymptotically equivalent to the usual Hausman fixed-vs-random effects test; with a balanced panel, the artificial regression and Hausman test statistics are numerically equal. See Arellano (1993) for an exact statement and the example below for a demonstration. Unlike the Hausman version, the test reported by xtoverid extends straightforwardly to heteroskedastic- and cluster-robust versions, and is guaranteed always to generate a nonnegative test
statistic." HTH, 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 06.05.2012 02:29, solafem7@yahoo.co.uk wrote:
The Hausman test is actually use to select between fixed and random effect. To know which one to chose you proceed as follow: if the p value is greater than 0.5 then the fixed effect(fe ) is not good chose random effect(re ) and otherwise if reverse is the case. Secondly, to test for autocorrelation after the. 'xtreg' test, you use 'xttest0' Sent from my BlackBerry wireless device from MTN -----Original Message----- From: Caliph Omar Moumin<sheikmoumin@yahoo.com> Sender: owner-statalist@hsphsun2.harvard.edu Date: Sat, 5 May 2012 07:46:33 To: statalist@hsphsun2.harvard.edu<statalist@hsphsun2.harvard.edu> Reply-To: statalist@hsphsun2.harvard.edu Subject: st: fixed effect or random effect model Dear allFor the past two weeks i spent to decide whether i apply fixed effect or random effect model in my strongly unbalanced panel data. But I couldn't decide it.These are the tests i applied so could you please give a minute and advice me what to apply? I understood the my hausman test impllies that i can apply either fixed or random effect modells. Is that so? If that is correct then i choose to apply the random effect model becuase of some time in-variant involved.What about Breusch-Pagan Lagrange multiplier (LM) test? I have no clue as to how interperate this test? Could any help me? xtdescribeid: 6, 9, ..., 809378 n = 14503 nadmission1: 1, 2, ..., 16 T = 16 Delta(nadmission1) = 1 unit Span(nadmission1) = 16 periods (id*nadmission1 uniquely identifies each observation) Distribution of T_i: min 5% 25% 50% 75% 95% max 1 1 1 1 1 2 16 Freq. Percent Cum. | Pattern ---------------------------+------------------ 13302 91.72 91.72 | 1............... 797 5.50 97.21 | 11.............. 160 1.10 98.32 | 111............. 97 0.67 98.99 | 1111............ 58 0.40 99.39 | 11111........... 31 0.21 99.60 | 111111.......... 29 0.20 99.80 | 1111111......... 12 0.08 99.88 | 11111111........ 8 0.06 99.94 | 111111111....... 9 0.06 100.00 | (other patterns) ---------------------------+------------------ 14503 100.00 | XXXXXXXXXXXXXXXXI want to compare between this two groupsxttab group; Overall Between Within group | Freq. Percent Freq. Percent Percent ----------+----------------------------------------------------- alcohol | 275 1.64 191 1.32 100.00 nonalcoh | 16443 98.36 14312 98.68 100.00 ----------+----------------------------------------------------- Total | 16718 100.00 14503 100.00 100.00 (n = 14503).quietly xtreg cost duration sex age group, fe;. estimates store fixed; . quietly xtreg cost duration sex age group, re; . estimates store random; hausman fixed random; ---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E. -------------+---------------------------------------------------------------- duration | 874.4642 944.5754 -70.11117 84.24204 ------------------------------------------------------------------------------ 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(1) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.69 Prob>chi2 = 0.4053Breusch-Pagan Lagrange multiplier (LM)test is performed as followsxtreg cost duration, re; xttest0; Breusch and Pagan Lagrangian multiplier test for random effects cost[id,t] = Xb + u[id] + e[id,t] Estimated results: | Var sd = sqrt(Var) ---------+----------------------------- cost | 2.27e+09 47647.13 e | 6.78e+08 26038.66 u | 1.66e+09 40752.23 Test: Var(u) = 0 chi2(1) = 59.40 Prob> chi2 = 0.0000 A test for heteroskedasticity is performed which shows presence xtreg cost duration, fe xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (14503) = 2.1e+36 Prob>chi2 = 0.0000 Kind Regards, Moumin Email: sheikmoumin@yahoo.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/ * * 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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