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Re: st: Panel with low within variance and -xtfmb-


From   "Ivan Etzo" <ivanetzo@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>, <nicola.baldini2@unibo.it>
Subject   Re: st: Panel with low within variance and -xtfmb-
Date   Tue, 9 Oct 2007 22:10:11 +0200 (ora legale Europa occidentale)

 
 Thank you Nicola,
 
I read the paper and used the command -xtfevd-. I then runned a fixed effect estimation using -regress- with -xi:- to generate groups dummies, which allows for the estimation of time invariant variables,  but I found quite different coefficient for the time invariant (while time variant are the same). I'm now wondering which is the difference of using the two different techinques..
 
Moreover, estimates of time variant variables from -xtfevd- are the same as those obtained with -xtreg- ( fe opt). I understand that -xtreg- is a within estimation which takes into account only variation across time (years) within each individual, the point is that my time variant variables are "almost time invariant", that is their within variation is very small compared to the between one.
 
Ivan
 
-------Original Message-------
 
Date: 10/06/07 13:12:03
Subject: Re: st: Panel with low within variance and -xtfmb-
 
Package -xtfevd- from http://www.polsci.org/pluemper may help. It is a module to estimate time-invariant and rarely changing variables in panel data models with fixed effects.
Nicola
 
At 02.33 05/10/2007 -0400, "Ivan Etzo" wrote:
>Hi,
>
>I'd like to test a basic form of my model (panel T=3D7 N=3D380) with= one regressor that is time invariant and two regressors that have low wi= thin variance with the following percentage
>
>Y: 15%
>X1: 0.6%
>X2:0.6%
>X3:0
>
>hausman test rejects the RE model (using -xtreg-) so I cannot estima= te the model with the time invariant regressor that for me it's very impo= rtant.
>
>I tried the -xtfmb- which is an implementation of the Fama and MacBe= th (1973) two step procedure, I report the very clear description of the = command from the help
>
>-xtfmb- "is an implementation of the Fama and MacBeth (1973) two ste= p procedure. The procedure is
>    as follows: In the fi= rst step, for each single time period a cross-sectional regression is
>=     performed. Then, in the second step, the final coeffic= ient estimates are obtained as the
>    average of the f= irst step coefficient estimates."
>
>The coefficient I obtain from this procedure are practically the sam= e with the RE model.. I attached the results  below. I wonder whethe= r the -xtfmb- produces inconsistent estimates as well.. any help would be= very appreciated.
>
>Ivan
>
>Random-effects GLS regression = ;Number of obs      =3D 2660
>Group varia= ble (i): region Number of groups   =3D 380
>
>R-sq:  within =3D 0.0097 Obs per group: min =3D = 7
>between =3D 0.8153 avg =3D 7.0
>overall =3D 0.= 7953 max =3D 7
>
>Random effects u_i ~ Gaussian Wald chi2(3)  &nbs=
>p;    =3D 1665.29
>corr(u_i, X) =3D 0 (assumed= ) Prob > chi2        =3D = 0.0000
>
>
>lnmig | Coef.   Std. Err. = z    P>|z|     [95% Conf. Inte= rval]
>
>lndpop | .9560363   .033868= 9 28.23   0.000     .8896544 1.02= 2418
>lnopop | 1.023108   .0338689 30.21  = ; 0.000     .9567262 1.08949
>lndist | -.= 3231335   .0587596 -5.50   0.000  &nbs=
>p; -.4383002 -.2079668
>_cons | -21.17337    .= 799259 -26.49   0.000    -22.73989 -19= =2E60685
>
>sigma_u | .68680964
>sigma_e |&n=
>bsp;.27499318
>rho | .86183578   (fraction of varia= nce due to u_i)
>
>
>
>Fama-MacBeth (1973) Two-Step procedure&nbs=
>p;Number of obs     =3D 2660
>  Num.= time periods =3D 7
>  F(  3,   &nbs=
>p; 6)     =3D 11189.96
>  Prob > = F          =3D 0.0000  avg. R-squared    =3D 0.7962
> &=
>nbsp;
> |        &nb=
>sp;   Fama-MacBeth
>lnmig |     = ; Coef.   Std. Err.      t P>|= t|     [95% Conf. Interval]
>   = ;
>lndpop |   .9552167   .0101939  &=
>nbsp; 93.70 0.000     .9302731 .9801604
>= lnopop |   1.017995   .0061794   164.7= 4 0.000     1.002874 1.033115
>lndist&nbs=
>p;|  -.3232731   .0117134   -27.60 0.000&nb=
>sp;   -.3519349 -.2946114
>_cons |  -21.08686&=
>nbsp;  .1716352  -122.86 0.000    -21.50683=  -20.66688
>
 



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