This is very useful. Thank you.
Thanks,
Ash.
--- Scott Merryman <[email protected]> wrote:
> ----- Original Message -----
> From: "ash_alankar" <[email protected]>
> To: <[email protected]>
> Sent: Thursday, June 19, 2003 4:57 PM
> Subject: Re: st: RE: Fixed Effects Panel Regression
> with correlation between
> panels
>
>
> > Hi Dolores,
> >
> > Thanks for the info. But when I mean correlation
> between panels I
> > mean that the errors for unit i are correlated
> with the errors for
> > unit j. I am assuming that the errors are
> independent across time
> > i.e. no autocorrelation.
> >
> > Do you know how to do this in a fixed effects
> framework? I was
> > thinking of using xtgls with a dummy of ones for
> each different unit
> > to capture the fixed effects, but was hoping that
> there might be a
> > better way.
> >
> > Thanks.
> > Ash.
>
>
> A possible alternative to fixed effects GLS would be
> to reshape the data and
> use -sureg-. If you constrain the coefficients to
> be equal across
> equations, it will give you the same results fixed
> effects GLS, however,
> sureg would allow the slope coefficients to vary by
> equation as well as
> contemporaneous correlation between the error terms
> across equations.
>
> Hope this helps,
> Scott
>
>
> Example:
>
> The do file:
> preserve
> use http://www.stata-press.com/data/r8/grunfeld.dta
> drop kstock
> keep if comp <4
>
> xtreg invest mvalue , fe
>
> xi i.com
> xtgls invest mva _*, panel(corr)
>
> drop _*
>
> reshape wide invest mvalue , i(year) j(company)
> forv i = 1/3 {
> local rhs " `rhs' (invest`i' mvalue`i' )"
> }
> di "`rhs'"
>
> constraint 1 [invest1]mvalue1 = [invest2]mvalue2
> constrain 2 [invest2]mvalue2 = [invest3]mvalue3
> sureg `rhs' ,const(1 2) corr
>
> restore
>
> The results:
>
> . preserve
>
> . use
> http://www.stata-press.com/data/r8/grunfeld.dta
>
> . drop kstock
>
> . keep if comp <4
> (140 observations deleted)
>
> .
> . xtreg invest mvalue , fe
>
> Fixed-effects (within) regression
> Number of obs =
> 60
> Group variable (i): company
> Number of groups =
> 3
>
> R-sq: within = 0.3725 Obs
> per group: min =
> 20
> between = 0.6451
> avg =
> 20.0
> overall = 0.5131
> max =
> 20
>
>
> F(1,56) =
> 33.25
> corr(u_i, Xb) = -0.3598 Prob
> > F =
> 0.0000
>
>
----------------------------------------------------------------------------
> --
> invest | Coef. Std. Err. t
> P>|t| [95% Conf.
> Interval]
>
-------------+--------------------------------------------------------------
> --
> mvalue | .1982563 .0343844 5.77
> 0.000 .1293759
> .2671366
> _cons | -171.4112 96.63739 -1.77
> 0.082 -364.9991
> 22.17679
>
-------------+--------------------------------------------------------------
> --
> sigma_u | 166.11924
> sigma_e | 155.73559
> rho | .53222836 (fraction of variance
> due to u_i)
>
----------------------------------------------------------------------------
> --
> F test that all u_i=0: F(2, 56) = 19.81
> Prob > F =
> 0.0000
>
> .
> . xi i.com
> i.company _Icompany_1-3 (naturally
> coded; _Icompany_1 omitted)
>
> . xtgls invest mva _*, panel(corr)
>
> Cross-sectional time-series FGLS regression
>
> Coefficients: generalized least squares
> Panels: heteroskedastic with cross-sectional
> correlation
> Correlation: no autocorrelation
>
> Estimated covariances = 6
> Number of obs =
> 60
> Estimated autocorrelations = 0
> Number of groups =
> 3
> Estimated coefficients = 4 Time
> periods =
> 20
> Wald
> chi2(3) =
> 244.45
> Log likelihood = -362.3947 Prob
> > chi2 =
> 0.0000
>
>
----------------------------------------------------------------------------
> --
> invest | Coef. Std. Err. z
> P>|z| [95% Conf.
> Interval]
>
-------------+--------------------------------------------------------------
> --
> mvalue | .1166626 .0323914 3.60
> 0.000 .0531767
> .1801485
> _Icompany_2 | 78.01431 86.04438 0.91
> 0.365 -90.62957
> 246.6582
> _Icompany_3 | -226.6125 89.96582 -2.52
> .012 -402.9423 -50.2827
> _cons | 102.4225 149.0675 0.69
> 0.492 -189.7444
> 394.5895
>
----------------------------------------------------------------------------
> --
>
> .
> . drop _*
>
> .
> . reshape wide invest mvalue , i(year) j(company)
> (note: j = 1 2 3)
>
> Data long -> wide
>
----------------------------------------------------------------------------
> -
> Number of obs. 60 -> 20
> Number of variables 5 -> 8
> j variable (3 values) company ->
> (dropped)
>
=== message truncated ===
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