Re: Re: [R] systemfit - SUR
contact@thomasalmer.com
contact at thomasalmer.com
Tue Nov 30 10:50:01 CET 2004
Arne Henningsen <ahenningsen at email.uni-kiel.de> schrieb am 30.11.2004,
10:20:29:
> On Tuesday 30 November 2004 09:34, contact at thomasalmer.com wrote:
> > Arne Henningsen schrieb am 29.11.2004,
> >
> > 17:02:12:
> > > On Monday 29 November 2004 16:42, contact at thomasalmer.com wrote:
> > > > Hello to everyone,
> > > >
> > > > I have 2 problems and would be very pleased if anyone can help me:
> > > >
> > > > 1) When I use the package "systemfit" for SUR regressions, I get two
> > > > different variance-covariance matrices when I firstly do the SUR
> > > > regression ("The covariance matrix of the residuals used for
> > > > estimation") and secondly do the OLS regressions. In the manual for
> > > > "systemfit" on page 14 I see however, that the variance-covariance
> > > > matrix for SUR is obtained from OLS. How can this be explained?
> > >
> > > Hi Thomas,
> > > I get identical residual covariance matrices:
> > >
> > > R> library(systemfit)
> > > R> data( kmenta )
> > > R> demand supply labels system fitols fitols$rcov
> > > [,1] [,2]
> > > [1,] 3.725391 4.136963
> > > [2,] 4.136963 5.784441
> > > R> fitsur fitsur$rcovest
> > > [,1] [,2]
> > > [1,] 3.725391 4.136963
> > > [2,] 4.136963 5.784441
> >
> > It is a pity, but my matrices are not as nice :-(
>
> Please show how you obtained these results.
I did not provide the steps, because the data is not public. But that is
what I did after defining the system and labels (like you & the
documentation):
fitols<-systemfit("OLS", system, labels)
fitsur<-systemfit("SUR", system, labels, maxit=100)
But after all I think your answers are sufficient. Thanks once again for
your support. R and the community are really excellent and a big thread
to competitors!
> This is what I did:
> R> data( kmenta )
> R> demand supply labels system
> R> # OLS estimation:
> R> fitols # (non-iterated) SUR estimation
> R> fitsur iterated SUR estimation
> R> fitsurit
> R> fitols$rcov
> [,1] [,2]
> [1,] 3.725391 4.136963
> [2,] 4.136963 5.784441
> R> fitsur$rcovest
> [,1] [,2]
> [1,] 3.725391 4.136963
> [2,] 4.136963 5.784441
> R> fitsurit$rcovest
> [,1] [,2]
> [1,] 6.199071 7.493383
> [2,] 7.493383 9.128547
>
>
> > An excerpt:
> > fitsur$rcovest
> > [,1] [,2] [,3] ...
> > [1,] 0.015097517 0.018005050
> > [2,] 0.018005050 0.276259834
> > ...
> >
> > fitols$rcov
> > [,1] [,2] [,3] ...
> > [1,] 1.010326e-02 0.0096103837
> > [2,] 9.610384e-03 0.2329884378
> > ...
> >
> > fitsur "The covariance matrix of the residuals used for estimation":
> > eq1 eq2 eq3 ...
> > eq1 0.01317429 0.01504719 0.007981307
> > eq2 0.01504719 0.25233860
> > ...
> >
> > fitols "The covariance matrix of the residuals":
> > eq1 eq2 eq3 ...
> > eq1 9.51154e-03 0.009137884 0.002648577
> > eq2 9.13788e-03 0.220435063
> > ...
> >
> > By the way: Why are the figures larger for SUR?
>
> OLS minimizes the residuals and, thus, also the variance of the residuals
> (=diagonal of the residual covariance matrix).
> Iterated SUR is equivalent to a maximum likelihood estimation. Maximizing the
> likelihood value is equivalent to minimizing the determinant of the residual
> covariance matrix. Thus, the determinant of the residual covariance matrix
> and not the residuals itself are minimized:
>
> R> det(fitols$rcov)
> [1] 4.434845
> R> det(fitsurit$rcov)
> [1] 0.4376941
> R> det(fitsurit$rcovest)
> [1] 0.4377184
>
> > > Did you do _iterated_ SUR?
> >
> > Yes:
> > "systemfit results
> > method: iterated SUR
> > convergence achieved after 30 iterations"
>
> If you use iterated SUR, the SUR estimations are iterated. In the first SUR
> estimation the residual covariance matrix of the OLS estimation is used. In
> all following iterations the residual covariance matrix of the previous step
> SUR estimation is used.
>
> > I do not know how to change that.
>
> Please read the documentation. It says to set argument "maxit" to 1 - or do
> not provide this argument, since 1 is the default.
>
> > > Best wishes,
> > > Arne
> >
> > THANKS A LOT FOR YOUR IMMEDIATE HELP!!!
> >
> > > > 2) Is there an easy possibility to test a) the OLS equations, and b)
> > > > the SUR system for SUR structures? In other words: Is the LM-Test from
> > > > Breusch and Pagan available in R?
>
> I don't understand what you want to test. Does the hausman test what you are
> looking for (see ?hausman.systemfit). If you have questions regarding this
> test, you might ask my co-author of systemfit, Jeff Hamann.
Primarily I want to test if the variance covariance matrix of the OLS
residuals is diagonal. But this can be done manually, of course.
> Best wishes,
> Arne
>
> > > > Thanks for the attention!
> > > >
> > > > Best Regards,
> > > > Thomas Almer
> > > >
> > > > ______________________________________________
> > > > R-help at stat.math.ethz.ch mailing list
> > > > https://stat.ethz.ch/mailman/listinfo/r-help
> > > > PLEASE do read the posting guide!
> > > > http://www.R-project.org/posting-guide.html
> > >
> > > --
> > > Arne Henningsen
> > > Department of Agricultural Economics
> > > University of Kiel
> > > Olshausenstr. 40
> > > D-24098 Kiel (Germany)
> > > Tel: +49-431-880 4445
> > > Fax: +49-431-880 1397
> > > ahenningsen at agric-econ.uni-kiel.de
> > > http://www.uni-kiel.de/agrarpol/ahenningsen/
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> > http://www.R-project.org/posting-guide.html
>
> --
> Arne Henningsen
> Department of Agricultural Economics
> University of Kiel
> Olshausenstr. 40
> D-24098 Kiel (Germany)
> Tel: +49-431-880 4445
> Fax: +49-431-880 1397
> ahenningsen at agric-econ.uni-kiel.de
> http://www.uni-kiel.de/agrarpol/ahenningsen/
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