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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/

**Follow-Ups**:**Re: st: heteroskedasticity test in panel data***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

**References**:**st: heteroskedasticity test in panel data***From:*"Jing Zhou" <jing.zhou@rmit.edu.au>

**Re: st: heteroskedasticity test in panel data***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

**Re: st: heteroskedasticity test in panel data***From:*"Jing Zhou" <jing.zhou@rmit.edu.au>

**Re: st: heteroskedasticity test in panel data***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

**Re: st: heteroskedasticity test in panel data***From:*"Jing Zhou" <jing.zhou@rmit.edu.au>

**Re: st: heteroskedasticity test in panel data***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

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