Just to add to Richard's explanation,
Subject: st: RE: SE's in Fixed Effects Model
Date sent: Wed, 23 Jun 2004 10:54:42 -0500
From: "Boylan, Richard" <[email protected]>
To: <[email protected]>
Send reply to: [email protected]
> In method (i) you need to adjust the degree of freedom. If you compare
> the standard errors in the two estimates you will notice that they are
> identical up to a scalar (approx = 1.46).
> Richard
The adjustment is necessary because in the second method (de-meaning
by hand) -regress- doesn't realize you have used up about 1k degrees
of freedom with your 1k fixed effects. Notice that the (correct) F-
stat of -areg- is reported as F( 2, 10062) whereas the (incorrect) F-
stat from -regress- is F( 2, 21598). Sqrt(21598/10062) = appx 1.46.
--Mark
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Tim R. Sass
> Sent: Wednesday, June 23, 2004 10:45 AM
> To: [email protected]
> Subject: st: SE's in Fixed Effects Model
>
>
> I am estimating a fixed-effects panel model using two methods: (i)
> manually
> demeaning the data and running regress, (ii) running areg on the
> original
> data. I am able to get identical estimated slope coefficients from both
>
> models. However, the standard errors are very different. The standard
> errors should be asymptotically equivalent, so given my large sample
> size
> (21,000+ observations) the reported standard errors ought to be quite
> close. I tried applying the standard error correction noted by Wiggins
> and
> Gould in the Stata 6 FAQ "How can I estimate a fixed-effects regresion
> with
> instrumental variables?", but as one would expect (given my large sample
>
> size) it made very little difference. Any ideas what may be going on
> here? My output is given below.
>
> Tim
>
>
>
> . areg nrtrgain nschools chgschl, absorb(student) ;
>
> Number of obs =
> 21601
> F( 2, 10062) =
> 219.77
> Prob > F =
> 0.0000
> R-squared =
> 0.3732
> Adj R-squared =
> -0.3455
> Root MSE =
> 31.382
>
> ------------------------------------------------------------------------
> ------
> nrtrgain | Coef. Std. Err. t P>|t|
> -------------+----------------------------------------------------------
> -------------+------
> nschools | -2.487567 1.967418 -1.26 0.206
> chgschl | -11.67239 .5572808 -20.95 0.000
> _cons | 20.07552 2.067988 9.71 0.000
>
>
> . reg de_nrtrgain de_nschools de_chgschl;
>
> Number of obs = 21601
> F( 2, 21598) = 471.74
> Prob > F = 0.0000
> R-squared = 0.0419
> Adj R-squared = 0.0418
>
> ------------------------------------------------------------------------
> ------
> de_nrtrgain | Coef. Std. Err. t P>|t|
> -------------+----------------------------------------------------------
> -------------+------
> de_nschools | -2.487567 1.342864 -1.85 0.064
> de_chgschl | -11.67239 .3803728 -30.69 0.000
> _cons | 20.07552 1.411508 14.22 0.000
> ------------------------------------------------------------------------
> ------
>
> . display _se[de_chgschl]*sqrt(e(df_r)/(e(df_r)-3+1));
> .38039039
>
>
>
>
> Tim R. Sass
> Professor Voice: (850)644-7087
> Department of Economics Fax: (850)644-4535
> Florida State University E-mail: [email protected]
> Tallahassee, FL 32306-2180 Internet:
> http://garnet.acns.fsu.edu/~tsass
>
>
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Prof. Mark E. Schaffer
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Department of Economics
School of Management & Languages
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