There are no dummies in the OLS regression; I am not expecting
identical results, but I am puzzled by the completely opposite results
(one positive coefficient, one negative) given by the standard OLS
estimation (a basic .reg var1 var2) and the FE estimations (.xtreg
var1 var2, fe i(x)), particularly as when looking at the data as a
whole (and theory) would seem to support the view of the OLS
estimation. My question is in essence whether one estimation 'voids'
the other; obviously only one is 'true'.
Robert Gaskell
On Wed, 2 Mar 2005 12:34:08 -0600, Gustavo Sanchez <[email protected]> wrote:
> Robert [[email protected]] asked:
>
> >I have been working on a panel dataset, looking at the relationship
> >between inflation and openness, but am slightly confused at to the
> >results I have been finding. When running OLS and Fixed-Effects (FE)
> >estimations completely different results are found. However, when the
> >i() option for the FE estimation is set to the year (which it
> >shouldn't be; it should be the country code), the panel data results
> >are almost exactly equal to OLS.
>
> >Am I making an error in my use of Stata, or is this a kind of
> >'pooling' (when setting i() to the year), implying that the 'correct'
> >value is as found by the 'correct' FE estimation. I have not been
> >able to find any direct information on this in the Stata documentation
> >or the net in general - any comments that you can give will be much
> >appreciated.
>
> Fixed effects using -xtreg,fe- and OLS using -regress- including the
> dummy variables produce the same slope parameters for the explanatory
> variables (see the example below).
> Are you including the dummy variables in your OLS regression?
>
> Here is the example:
>
> . sysuse auto,clear
> (1978 Automobile Data)
>
> . xtreg price mpg weight,fe i(rep78)
>
> Fixed-effects (within) regression Number of obs =
> 69
> Group variable (i): rep78 Number of groups =
> 5
>
> R-sq: within = 0.3591 Obs per group: min =
> 2
> between = 0.0002 avg =
> 13.8
> overall = 0.2994 max =
> 30
>
> F(2,62) =
> 17.37
> corr(u_i, Xb) = -0.4414 Prob > F =
> 0.0000
> ----------------------------------------------------------------------------
> --
> price | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> mpg | -63.0971 87.45276 -0.72 0.473 -237.9127
> 111.7185
> weight | 2.093066 .636901 3.29 0.002 .8199193
> 3.366213
> _cons | 1143.134 3565.726 0.32 0.750 -5984.65
> 8270.918
> -------------+--------------------------------------------------------------
> --
> sigma_u | 1287.8103
> sigma_e | 2424.057
> rho | .22011463 (fraction of variance due to u_i)
> ----------------------------------------------------------------------------
> --
> F test that all u_i=0: F(4, 62) = 1.66 Prob > F =
> 0.1707
>
> . xi:regress price mpg weight i.rep78
> i.rep78 _Irep78_1-5 (naturally coded; _Irep78_1 omitted)
>
> Source | SS df MS Number of obs =
> 69
> -------------+------------------------------ F( 6, 62) =
> 6.03
> Model | 212481723 6 35413620.6 Prob > F =
> 0.0001
> Residual | 364315236 62 5876052.19 R-squared =
> 0.3684
> -------------+------------------------------ Adj R-squared =
> 0.3073
> Total | 576796959 68 8482308.22 Root MSE =
> 2424.1
>
> ----------------------------------------------------------------------------
> --
> price | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> mpg | -63.0971 87.45276 -0.72 0.473 -237.9127
> 111.7185
> weight | 2.093066 .636901 3.29 0.002 .8199193
> 3.366213
> _Irep78_2 | 753.7024 1919.763 0.39 0.696 -3083.849
> 4591.254
> _Irep78_3 | 1349.361 1772.706 0.76 0.449 -2194.228
> 4892.95
> _Irep78_4 | 2030.47 1810.09 1.12 0.266 -1587.848
> 5648.788
> _Irep78_5 | 3376.91 1900.17 1.78 0.080 -421.4749
> 7175.296
> _cons | -598.9665 3960.904 -0.15 0.880 -8516.701
> 7318.768
> ----------------------------------------------------------------------------
> --
>
> Sincerely,
>
> --Gustavo
> [email protected]
>
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>
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