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Re: st: fixed effect or random effect model
From
Caliph Omar Moumin <[email protected]>
To
"[email protected]" <[email protected]>
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: [email protected]
----- Original Message -----
From: John Antonakis <[email protected]>
To: [email protected]
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: [email protected]
>
>
>
> ----- Original Message -----
> From: John Antonakis<[email protected]>
> To: [email protected]
> 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, [email protected] 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<[email protected]>
>> Sender: [email protected]
>> Date: Sat, 5 May 2012 07:46:33
>> To: [email protected]<[email protected]>
>> Reply-To: [email protected]
>> 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: [email protected]
>>
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