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| From | Caliph Omar Moumin <sheikmoumin@yahoo.com> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | Re: st: fixed effect or random effect model |
| Date | Sun, 6 May 2012 04:58:34 -0700 (PDT) |
Many thanks to you John, that was so helpful. Kind 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 1:49 PM Subject: Re: st: fixed effect or random effect model Right. You can go with the random-effects model; also, that the Breusch-Pagan test is significant means that there is significant variance in uj (in the random-effects specification); i.e., uj is not zero. Thus, you can use a random-effects model. 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 = 0.0000 > > > > Kind 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/ * * 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/