From: Adaikalavan Ramasamy <ramasamy_at_cancer.org.uk>

Date: Tue 06 Sep 2005 - 02:30:12 EST

R-help@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 Received on Tue Sep 06 02:05:16 2005

Date: Tue 06 Sep 2005 - 02:30:12 EST

You will need to ensure that firm is a factor and not numerical (i.e. continuous). Here is an example

firm <- factor( sample(1:3, 20, replace=T) ) x1 <- runif(20) y <- rnorm(20)

summary( fit <- lm( y ~ -1 + x1 + firm ) )
...

Coefficients:

Estimate Std. Error t value Pr(>|t|)

x1 -0.04964 0.74861 -0.066 0.948 firm1 0.10732 0.48269 0.222 0.827 firm2 0.27548 0.48781 0.565 0.580 firm3 -0.07651 0.53384 -0.143 0.888

NB : The "-1" in the formula forces each firm to have its own intercept.

Use model.matrix, you will see the dummy variables created within lm().

model.matrix( fit )

x1 firm1 firm2 firm3 1 0.6641647 0 1 0 2 0.5142712 1 0 0 3 0.2197956 1 0 0 4 0.3211675 0 1 0 5 0.1892449 1 0 0 6 0.7740754 0 0 1 7 0.3486932 0 1 0 8 0.2116816 0 0 1 9 0.2426825 0 1 0 10 0.2219768 1 0 0 11 0.9328514 1 0 0 12 0.7880405 0 0 1 13 0.8673492 0 1 0 14 0.1777998 0 1 0 15 0.3178498 1 0 0 16 0.3379726 0 0 1 17 0.9193359 1 0 0 18 0.6998152 0 1 0 19 0.2825702 0 0 1 20 0.6139586 1 0 0

Regards, Adai

On Mon, 2005-09-05 at 15:53 +0100, Tobias Muhlhofer wrote:

> So are you guys saying to me that if I have variable firm which is the

*> factor of all firm identifiers, I could just go
**>
**> lm(y ~ x + firm)
**>
**> and that will implicitly include a dummy for each level of factor firm,
**> thus making this a fixed effects (aka LSDV) model?
**>
**> T
**>
**>
**> Jean Eid wrote:
**> > You can turn the identity vector of the firms into a factor and do lm ....
**> >
**> > Jean
**> >
**> > On Mon, 5 Sep 2005, Tobias Muhlhofer wrote:
**> >
**> >
**> >>Hi, all!
**> >>
**> >>Anyone know an easy way to specify the following model.
**> >>
**> >>Panel dataset, with stock through time, by firm.
**> >>
**> >>I want to run a model of y on a bunch of explanatory variables, and one
**> >>dummy for each firm, which is 1 for observations that come from firm i,
**> >>and 0 everywhere else. I have over 200 firms (and a factor variable that
**> >> contains a firm identifier).
**> >>
**> >>Any easy way of going about this, without having to define all these
**> >>dummies? I checked lme() with random = ~ 1|firm, but the problem is that
**> >>these are random effects, i.e. that there are firm-by-firm disturbance
**> >>terms and overall disturbance terms, whereas I want just overall
**> >>disturbance terms. This is generally called a "fixed effects" model,
**> >>although it seems like the term "fixed effects" is being used somewhat
**> >>differently in the context of the nlme package.
**> >>
**> >>Toby
**> >>
**> >>--
**> >>**************************************************************************
**> >>When Thomas Edison invented the light bulb he tried over 2000
**> >>experiments before he got it to work. A young reporter asked
**> >>him how it felt to have failed so many times. He said
**> >>"I never failed once. I invented the light bulb.
**> >>It just happened to be a 2000-step process."
**> >>
**> >>______________________________________________
**> >>R-help@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
**> >>
**> >
**> >
**> >
*

>

R-help@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 Received on Tue Sep 06 02:05:16 2005

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