Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: heteroskedasticity test in panel data


From   "Jing Zhou" <jing.zhou@rmit.edu.au>
To   <statalist@hsphsun2.harvard.edu>
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" <Michael.Norman.Mitchell@gmail.com> 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"<Michael.Norman.Mitchell@gmail.com>  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"<Michael.Norman.Mitchell@gmail.com>   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
>>>
>>>
>>>
>>>
>>>
>>> *
>>> *   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/

*
*   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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index