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Re: st: heteroskedasticity test in panel data
From
"Jing Zhou" <[email protected]>
To
<[email protected]>
Subject
Re: st: heteroskedasticity test in panel data
Date
Tue, 27 Jul 2010 17:51:49 +1000
following is the command and corresponding output.
. xtgls roa tlaw genvironment aci2 size leverage age, igls panels (heteroskedastic)
Iteration 1: tolerance = .01281716
Iteration 2: tolerance = .01676558
Iteration 3: tolerance = .25025852
Iteration 4: tolerance = .00706137
Iteration 5: tolerance = .04061494
Iteration 6: tolerance = .03815978
Iteration 7: tolerance = .03675714
Iteration 8: tolerance = .02342555
Iteration 9: tolerance = .00073142
Iteration 10: tolerance = .00832932
Iteration 11: tolerance = 3.144e-06
Iteration 12: tolerance = 1.718e-07
Iteration 13: tolerance = .1305574
Iteration 14: tolerance = .11548056
Iteration 15: tolerance = .08959096
Iteration 16: tolerance = .02050352
Iteration 17: tolerance = .006188
Iteration 18: tolerance = .02034936
Iteration 19: tolerance = .01040934
Iteration 20: tolerance = .0073191
Iteration 21: tolerance = .00270878
Iteration 22: tolerance = .00243333
Iteration 23: tolerance = .00237504
Iteration 24: tolerance = .14171418
Iteration 25: tolerance = .00958554
Iteration 26: tolerance = .00850144
Iteration 27: tolerance = .00094421
Iteration 28: tolerance = .02799819
Iteration 29: tolerance = 8.475e-06
Iteration 30: tolerance = .00224329
Iteration 31: tolerance = .11496823
Iteration 32: tolerance = .0108985
Iteration 33: tolerance = .00491695
Iteration 34: tolerance = .01146044
Iteration 35: tolerance = .11495675
Iteration 36: tolerance = .00775622
Iteration 37: tolerance = .00769652
Iteration 38: tolerance = .00452005
Iteration 39: tolerance = .00376106
Iteration 40: tolerance = .00165737
Iteration 41: tolerance = .00165462
Iteration 42: tolerance = .00148306
Iteration 43: tolerance = .00311958
Iteration 44: tolerance = .00028596
Iteration 45: tolerance = .00036032
Iteration 46: tolerance = .00211196
Iteration 47: tolerance = .0600343
Iteration 48: tolerance = .0023866
Iteration 49: tolerance = .01014685
Iteration 50: tolerance = .06387619
Iteration 51: tolerance = .07202545
Iteration 52: tolerance = .02556249
Iteration 53: tolerance = .00008123
Iteration 54: tolerance = .00004186
Iteration 55: tolerance = .00175812
Iteration 56: tolerance = .05552171
Iteration 57: tolerance = .01552817
Iteration 58: tolerance = .01716332
Iteration 59: tolerance = .02063742
Iteration 60: tolerance = .01274508
Iteration 61: tolerance = .00920043
Iteration 62: tolerance = .12077282
Iteration 63: tolerance = .00905253
Iteration 64: tolerance = .01079828
Iteration 65: tolerance = .03328352
Iteration 66: tolerance = .01233767
Iteration 67: tolerance = .00929827
Iteration 68: tolerance = .05281334
Iteration 69: tolerance = .03867031
Iteration 70: tolerance = .01011156
Iteration 71: tolerance = .00011164
Iteration 72: tolerance = .00999907
Iteration 73: tolerance = 7.644e-08
Cross-sectional time-series FGLS regression
Coefficients: generalized least squares
Panels: heteroskedastic
Correlation: no autocorrelation
Estimated covariances = 621 Number of obs = 2916
Estimated autocorrelations = 0 Number of groups = 621
Estimated coefficients = 3 Obs per group: min = 1
avg = 4.695652
max = 10
Wald chi2(3) = 4.40e+13
Log likelihood = 4073.23 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
roa | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tlaw | .000556 8.73e-09 6.4e+04 0.000 .000556 .000556
genvironment | .0013927 4.93e-08 2.8e+04 0.000 .0013926 .0013928
aci2 | (omitted)
size | .0003605 5.94e-08 6065.32 0.000 .0003604 .0003606
leverage | (omitted)
age | -.0030722 6.90e-09 -4.5e+05 0.000 -.0030722 -.0030722
_cons | (omitted)
------------------------------------------------------------------------------
. estimates store hetero
. xtgls roa tlaw genvironment aci2 size leverage age
Cross-sectional time-series FGLS regression
Coefficients: generalized least squares
Panels: homoskedastic
Correlation: no autocorrelation
Estimated covariances = 1 Number of obs = 2916
Estimated autocorrelations = 0 Number of groups = 621
Estimated coefficients = 7 Obs per group: min = 1
avg = 4.695652
max = 10
Wald chi2(6) = 372.93
Log likelihood = 855.1189 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
roa | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tlaw | .0010038 .0001621 6.19 0.000 .000686 .0013216
genvironment | .0015588 .0021767 0.72 0.474 -.0027075 .0058251
aci2 | .0244187 .006892 3.54 0.000 .0109106 .0379268
size | .0202311 .0034783 5.82 0.000 .0134137 .0270485
leverage | -.0176257 .0013651 -12.91 0.000 -.0203012 -.0149502
age | -.0039722 .0007833 -5.07 0.000 -.0055075 -.002437
_cons | -.4569186 .0729608 -6.26 0.000 -.5999192 -.3139181
------------------------------------------------------------------------------
. local df=e(N_g)-1
. display e(N_g)-1
620
.
end of do-file
. lrtest hetero ., df(620)
Likelihood-ratio test LR chi2(620)= -6436.22
(Assumption: hetero nested in .) Prob > chi2 = 1.0000
Thank you.
Jing
>>> "Michael N. Mitchell" <[email protected]> 27/07/2010 5:37 pm >>>
Dear Jing
Based on reading the FAQ (at http://www.stata.com/support/faqs/stat/panel.html) and the
results you report, it sounds like your data do not show heteroskedasticity across panels.
But, at the same time, I share your concern about getting a p value of 1.000. Perhaps you
could post your commands and output (suppressing any output that you need to suppress for
privacy/confidentiality) so we might be able to see any clues of trouble.
Best regards,
Michael N. Mitchell
Data Management Using Stata - http://www.stata.com/bookstore/dmus.html
A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
Stata tidbit of the week - http://www.MichaelNormanMitchell.com
On 2010-07-26 11.40 PM, Jing Zhou wrote:
> Dear Michael,
>
> Thank you for your kind assistance. follow the recommended commands on FAQs, and your suggestion, i run this test in stata. the result is however a little weird. the value of df is large (620), and Prob> chi2 = 1.0000. Can i just conclude that my panel data is not exposed to heteroskedasticity from this result? or there still exists some problem in the process? Thanks!
>
> Jing
>
>
>>>> "Michael N. Mitchell"<[email protected]> 27/07/2010 3:24 pm>>>
> Dear Jing
>
> Based on your example, it looks like you could do this...
>
> . xtgls..., igls panels (heteroskedastic)
> . estimates store hetero
> . xtgls...
> . display e(N_g)-1
>
> The last command will show, I believe, the number of groups minus 1. It looks like your
> example uses this for the degrees of freedom. Say that number was 157. You could then type
>
> . lrtest hetero ., df (157)
>
> and it looks like it would use 157 as the df. I am out of my element here, so I trust
> that someone else will correct me if I am off base. But I hope this helps.
>
> Michael N. Mitchell
> Data Management Using Stata - http://www.stata.com/bookstore/dmus.html
> A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
> Stata tidbit of the week - http://www.MichaelNormanMitchell.com
>
>
>
> On 2010-07-26 9.58 PM, Jing Zhou wrote:
>> thank you Michael, for the command "lrtest hetero ., df ('df')", how can i get the value of df?
>>
>> Jing
>>
>>>>> "Michael N. Mitchell"<[email protected]> 27/07/2010 2:23 pm>>>
>> Greetings
>>
>> I wonder if this would help...
>>
>> . set matsize 800
>>
>> (or select another number in place of 800).
>>
>> Hope that helps,
>>
>> Michael N. Mitchell
>> Data Management Using Stata - http://www.stata.com/bookstore/dmus.html
>> A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
>> Stata tidbit of the week - http://www.MichaelNormanMitchell.com
>>
>>
>>
>> On 2010-07-26 7.57 PM, Jing Zhou wrote:
>>> Dear All,
>>>
>>> I am going to test the heteroskedasticity in my panel data. by using the recommended commands on FAQ which are specified as:
>>>
>>> xtgls..., igls panels (heteroskedastic)
>>> estimates store hetero
>>> xtgls...
>>> local df=e (N_g)-1
>>> lrtest hetero., df ('df')
>>>
>>> the result shows wrong information as "matsize too small - should be at least 621". Could you please advise me what is the potential cause to this problem? and how can i refine it?
>>>
>>> Many thanks!
>>>
>>> Jing
>>>
>>>
>>>
>>>
>>>
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