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Re: st: xtreg fixed effect
From
Fernando Rios Avila <[email protected]>
To
[email protected]
Subject
Re: st: xtreg fixed effect
Date
Wed, 9 Nov 2011 09:38:33 -0500
I cant say anything about that, since i dont know what you are trying
to test nor why you need all those variables. My best answer is, get
more data, or explain what the nature of your model.
On Wed, Nov 9, 2011 at 9:34 AM, donsaane <[email protected]> wrote:
> Thanks Fernando,
> So if I understand I have to use another model instead of panel fixed
> effect?
>
> -----Message d'origine-----
> De : [email protected]
> [mailto:[email protected]] De la part de Fernando Rios
> Avila
> Envoyé : mercredi 9 novembre 2011 06:50
> À : [email protected]
> Objet : Re: st: xtreg fixed effect
>
> Donsaane,
> It seems to me that the problem is that you just dont have sufficient
> observations.
> From your output u have 82 observations and 70 groups, what leaves
> space for estimating 12 more parameters. If you include 13 variables,
> the information is just not long enough for such analysis. Perhaps you
> are doing this on purpose, but you only have 2 points of observations
> at MOST for each individual, which i think is not enough to estimate a
> panel fixed effect data.
>
> Fernando
>
> On Wed, Nov 9, 2011 at 4:42 AM, Nick Cox <[email protected]> wrote:
>> Not so:
>>
>> webuse nlswork
>> xtset idcode
>> generate age2 = age^2
>> xtreg ln_w grade age*
>>
>> <all work fine>
>>
>> . gen badvar = string(_n)
>>
>> . xtreg ln_w grade age* badvar
>> no observations
>> r(2000);
>>
>> Nick
>> [email protected]
>>
>> Vikram Finavker
>>
>> Have you checked the 3 variables which you are adding. If they are
>> string then stata will give you these message.
>>
>> On 9 Nov 2011, at 03:30 AM, donsaane <[email protected]> wrote:
>>
>>> I am running a fixed effect model. I have to run this model with 16
>>> variables but I can only run with 13 variables because the R-squared is
>>> 0.984. When I add the 3 others variables, stata said: 'insufficient
>>> obervations' although these variables have many observations. Is there a
>>> problem with that? I f yes how can I solve that.
>>> PS: I'm working on a balanced panel
>>> Here the example of my output
>>>
>>>
>>> xtreg emploi c30a j30f c30b d30a d30b e30 g30a h30 i30 j30a j30b, fe
>>> vce(robust)
>>>
>>> Fixed-effects (within) regression Number of obs =
>>> 82
>>> Group variable: panelid Number of groups =
>>> 70
>>>
>>> R-sq: within = 0.9874 Obs per group: min =
>>> 1
>>> between = 0.0672 avg =
>>> 1.2
>>> overall = 0.0605 max =
>>> 2
>>>
>>> F(11,69) =
>>> 118.55
>>> corr(u_i, Xb) = -0.9701 Prob > F =
>>> 0.0000
>>>
>>> (Std. Err. adjusted for 70 clusters in
>>> panelid)
>>>
> ----------------------------------------------------------------------------
>>> --
>>> | Robust
>>> emploi | Coef. Std. Err. t P>|t| [95% Conf.
>>> Interval]
>>>
> -------------+--------------------------------------------------------------
>>> --
>>> c30a | .9143767 .059555 15.35 0.000 .7955678
>>> 1.033186
>>> j30f | -.47526 .030562 -15.55 0.000 -.5362296
>>> -.4142904
>>> c30b | .0024208 .0192561 0.13 0.900 -.035994
>>> .0408356
>>> d30a | -.2705821 .0331653 -8.16 0.000 -.3367451
>>> -.2044191
>>> d30b | -.5215098 .0365427 -14.27 0.000 -.5944105
>>> -.4486091
>>> e30 | -.4530234 .0565547 -8.01 0.000 -.565847
>>> -.3401997
>>> g30a | .5852609 .0403684 14.50 0.000 .5047281
>>> .6657937
>>> h30 | -.1785489 .0161245 -11.07 0.000 -.2107165
>>> -.1463814
>>> i30 | -.0622 .0232747 -2.67 0.009 -.1086317
>>> -.0157683
>>> j30a | -.6889813 .0697342 -9.88 0.000 -.8280972
>>> -.5498655
>>> j30b | .4019738 .0364463 11.03 0.000 .3292655
>>> .4746821
>>> _cons | 2.07585 .2529012 8.21 0.000 1.571326
>>> 2.580374
>>>
> -------------+--------------------------------------------------------------
>>> --
>>> sigma_u | 1.915378
>>> sigma_e | .13146307
>>> rho | .99531125 (fraction of variance due to u_i)
>>>
> ----------------------------------------------------------------------------
>>> --
>>>
>>> . xtreg emploi c30a j30f c30b d30a d30b e30 g30a h30 i30 j30a j30b j30c,
> fe
>>> vce(robust)
>>> insufficient observations
>>> r(2001);
>>>
>>
>>
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>>
>
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