Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

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

From |
"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: heteroskedasticity test in panel data |

Date |
Tue, 27 Jul 2010 18:52:41 -0700 |

Dear Jing (and all)

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 2010-07-27 6.11 PM, Jing Zhou wrote:

Dear Michael, follow your suggestions, i rescaled my variables in panel model, but still the problems exist. I tried another method. I reexamined the first model by removing "igls", and there is no variable omitted and the SEs are acceptable. but when I run "lrtest hetero ., df('df')", the result shows wrong information as "hetero does not contain scalar e(ll)". I attached the outcomes below. Could you advise me why it happens? Thank you. Jing . xtgls roa tlawn genvironment aci2 size leverage age, panels (heteroskedastic) 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 = 7 Obs per group: min = 1 avg = 4.695652 max = 10 Wald chi2(6) = 15124.70 Prob> chi2 = 0.0000 ------------------------------------------------------------------------------ roa | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tlawn | .0713694 .0024424 29.22 0.000 .0665823 .0761564 genvironment | .0012895 .0004232 3.05 0.002 .0004601 .0021188 aci2 | .0240381 .0015218 15.80 0.000 .0210555 .0270207 size | .0182393 .0006986 26.11 0.000 .01687 .0196085 leverage | -.0391871 .0030122 -13.01 0.000 -.0450909 -.0332833 age | -.0039249 .0001145 -34.27 0.000 -.0041493 -.0037004 _cons | -.3728419 .0144485 -25.80 0.000 -.4011604 -.3445234 ------------------------------------------------------------------------------ . estimates store hetero . . xtgls roa tlawn 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] -------------+---------------------------------------------------------------- tlawn | .1003806 .0162142 6.19 0.000 .0686014 .1321598 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 | -.4569187 .0729608 -6.26 0.000 -.5999192 -.3139181 ------------------------------------------------------------------------------ . . local df=e(N_g)-1 . . display e(N_g)-1 620 . lrtest hetero ., df(620) hetero does not contain scalar e(ll) r(498);"Michael N. Mitchell"<Michael.Norman.Mitchell@gmail.com> 28/07/2010 2:03 am>>>Dear Jing Perhaps my last bit of advice was slightly misguided... maybe the model would benefit from a rescaling of the outcome variable. I am just very concerned about those extremely tiny standard errors, because computationally I wonder how close they are to 0. (And, thus how many other quantities are getting close to 0). If that does not solve the problem of your predictors being dropped in the first model, then that problem needs to be solved first. My hunch is that it is an issue of multicollinearity, but I am unaware of how to examine that in the context of a panel model. Maybe others have suggestions? Best luck! 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 2010-07-27 1.30 AM, Jing Zhou wrote:Dear Michael, Thank you for your suggestions. in fact the predictor in my model is a percentage which should not be very large. however, i still follow your suggestions to divide other large regressors. the results are unchanged (p value of 1.000), and in the first model some regressors are also omitted. I am wondering how this omitted variables happened? Thanks. Jing"Michael N. Mitchell"<Michael.Norman.Mitchell@gmail.com> 27/07/2010 6:02 pm>>>Dear Jing I think this is very informative. I notice two issues... 1) The terms -aci2-, -leverage-, and the constant (_cons) were omitted from the first model. 2) The standard errors are extremely tiny, and the coefficients for the terms that are present are very small. I wonder if you have an issue with the scaling of the variables, and that your model is not being estimated very stably because the units of the variables are very large. You might try dividing the predictor by a constant, e.g. . generate tlawnew = tlaw / 1000 and then entering the "new" variable. I think this might lead to a more stable estimate of the first model, and then different results with respect to the chi-squared test. 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 2010-07-27 12.51 AM, Jing Zhou wrote: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/

* * 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**:**st: Re: heteroskedasticity test in panel data***From:*David De Boeck <david.deboeck@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>

**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>

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

- Prev by Date:
**Re: st: Dropping observations and creating a balanced panel** - Next by Date:
**Re: st: Classic Statistical Software Review** - Previous by thread:
**Re: st: heteroskedasticity test in panel data** - Next by thread:
**st: Re: heteroskedasticity test in panel data** - Index(es):