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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 13:49:29 +0200 |
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 06.05.2012 13:34, Caliph Omar Moumin wrote:
Thank you John You told me important info. i applied it and the result as shown below is Sargan-Hansen statistic 0.051 Chi-sq(1) P-value = 0.8219. So i think this is same result as hausman test. Meaning that we failed to reject null (both fixed and random effect model are ok).Therefore in My case i want to choose random effect model. if you think otherwise, could you please let me know? does Breusch and Pagan Lagrangian multiplier test for random effects makes any change of my choice of random based on Sargan-Hansen statistic? The result of Breusch and Pagan Lagrangian multiplier test is chibar2(01) = 59.40; Prob> chibar2 = 0.0000.Thank you again John xtreg cost duration sex age group, re cluster(id) Random-effects GLS regression Number of obs = 16718 Group variable: id Number of groups = 14503 R-sq: within = 0.0392 Obs per group: min = 1 between = 0.0535 avg = 1.2 overall = 0.0578 max = 16 Wald chi2(4) = 371.51 corr(u_i, X) = 0 (assumed) Prob> chi2 = 0.0000 (Std. Err. adjusted for 14503 clusters in id) ------------------------------------------------------------------------------ | Robust cost | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- duration | 944.5671 152.539 6.19 0.000 645.5962 1243.538 sex | -4476.141 781.0165 -5.73 0.000 -6006.905 -2945.377 age | 306.88 20.33477 15.09 0.000 267.0246 346.7354 group | 4442.876 1727.691 2.57 0.010 1056.665 7829.087 _cons | 922.7695 3769.766 0.24 0.807 -6465.835 8311.374 -------------+---------------------------------------------------------------- sigma_u | 40329.125 sigma_e | 26038.659 rho | .70578153 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . xtoverid Test of overidentifying restrictions: fixed vs random effects Cross-section time-series model: xtreg re robust cluster(id) Sargan-Hansen statistic 0.051 Chi-sq(1) P-value = 0.8219 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 chibar2(01) = 59.40 Prob> chibar2 = 0.0000Kind Regards,Caliph Omar Moumin Email: sheikmoumin@yahoo.com ----- Original Message ----- From: John Antonakis<John.Antonakis@unil.ch> To: statalist@hsphsun2.harvard.edu Cc: Sent: Sunday, May 6, 2012 10:31 AM Subject: Re: st: fixed effect or random effect model It would be more correct to say that if the p-value for the Hausman test, where you compare random vs fixed-effects, is< .05 then the random-effects estimator is no good (i.e., the test is in the form "hausman fe re"). The fixed-effects estimator is consistent; however, the random-effects estimator is more efficient. If the estimates using random effects are not significantly different from the fixed-effects estimator (i.e., the p-value is> .05) then you can retain the random-effects estimator. 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 all For 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? xtdescribe id: 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 | XXXXXXXXXXXXXXXX I want to compare between this two groups xttab 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 = 0.4053 Breusch-Pagan Lagrange multiplier (LM)test is performed as follows xtreg 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/* * 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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