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From |
Partha Deb <partha.deb@hunter.cuny.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Questions about xtnbreg ... , fe |

Date |
Thu, 18 Aug 2005 13:23:29 -0400 |

John,

1. It's not uncommon in nonlinear models for predicted means not to coincide with their sample counterparts. Cauchy-Schwarz type-inequalities at work.

2. The fact that constant-within-group variables don't drop out in the FE-NB model bothers many people, but is correct. The FE-NB (like the linear-FE model) conditions on a sufficient statistic for the Fixed effect. In the linear (and a class of nonlinear cases), this eliminates all constant-within-group variables, but this is not a general statement. And it does not apply in the NB case.

Hope this helps.

Partha

************************************************************************

Partha Deb ph: (212) 772-5435

Department of Economics fax: (212) 772-5398

Hunter College http://urban.hunter.cuny.edu/~deb/

Emancipate yourselves from mental slavery

None but ourselves can free our minds.

- Bob Marley

************************************************************************

--On Thursday, August 18, 2005 9:53 AM +0900 John Plummer <john.plummer@flinders.edu.au> wrote:

I have a data set in which counts were collected at 2 time points under 2 conditions for each subject. There are no missing data. Data for one subject look like this: id time condit~n diagno~s count 5 0 0 2 0 5 0 1 2 8 5 1 0 2 5 5 1 1 2 7 Question 1. I fitted a fixed effects negative binomial model including time, condition, and their interaction: . xi: xtnbreg count i.time*condition, i(id) fe irr nolog i.time _Itime_0-1 (naturally coded; _Itime_0 omitted) i.time*condit~n _ItimXcondi_# (coded as above) Conditional FE negative binomial regression Number of obs = 544 Group variable (i): id Number of groups = 136 Obs per group: min = 4 avg = 4.0 max = 4 Wald chi2(3) = 154.25 Log likelihood = -646.33027 Prob > chi2 = 0.0000 ------------------------------------------------------------------------- ----- count | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------- ----- _Itime_1 | 2.069823 .1629906 9.24 0.000 1.773798 2.415249 condition | 1.201935 .1071924 2.06 0.039 1.009179 1.431508 _ItimXcond~1 | .3432684 .0418947 -8.76 0.000 .2702388 .4360336 ------------------------------------------------------------------------- ----- From the IRRs I calculated a table of predicted relative cell counts, with time = 0 and condition = 0 as the reference category. They are: ------------------------ | condition time | 0 1 ----------+------------- 0 | 1 1.202 1 | 2.07 .854 ------------------------ The means of observed cell counts are below: ---------------------------- | condition time | 0 1 ----------+----------------- 0 | 2.09559 2.64706 1 | 4.36765 1.91176 ---------------------------- These are in the ratios below, which differ from the predicted ratios: ------------------------ | condition time | 0 1 ----------+------------- 0 | 1 1.263 1 | 2.084 .912 ------------------------ I had expected that the observed and predicted relative cell frequencies would be the same for the fitted model. Can anyone explain why they are not? Question 2. I believed that variables which are constant within ID could not be used in a fixed effects model. Am I wrong? It seems that I am, as illustrated by the inclusion of diagnosis (which is constant within a subject). . xi: xtnbreg count diagnosis i.time*condition, i(id) fe irr nolog i.time _Itime_0-1 (naturally coded; _Itime_0 omitted) i.time*condit~n _ItimXcondi_# (coded as above) Conditional FE negative binomial regression Number of obs = 544 Group variable (i): id Number of groups = 136 Obs per group: min = 4 avg = 4.0 max = 4 Wald chi2(4) = 172.78 Log likelihood = -643.28819 Prob > chi2 = 0.0000 ------------------------------------------------------------------------- ----- count | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------- ----- diagnosis | 7.784788 5.952364 2.68 0.007 1.739424 34.8408 _Itime_1 | 2.072968 .1601387 9.44 0.000 1.781708 2.411841 condition | 1.180407 .1038162 1.89 0.059 .9935027 1.402473 _ItimXcond~1 | .3444533 .0414192 -8.86 0.000 .2721301 .4359976 ------------------------------------------------------------------------- ----- Question 3 Can anyone offer advice as to how to assess goodness of fit for xtnbreg ... , fe? (some models I want to fit are rather more complex than the above examples). Any advice or comments will be appreciated Thanks John Plummer * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

************************************************************************ Partha Deb ph: (212) 772-5435 Department of Economics fax: (212) 772-5398 Hunter College http://urban.hunter.cuny.edu/~deb/ Emancipate yourselves from mental slavery None but ourselves can free our minds. - Bob Marley ************************************************************************ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: composite kappa & intercenter variability in kappas***From:*Richard_Lenhardt@rush.edu

**st: Questions about xtnbreg ... , fe***From:*John Plummer <john.plummer@flinders.edu.au>

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