Dear list, CC Dr Croissant,

I'm tryng to use plm with timeXgroup effects (in addition to the twoway effects), and running into problems with dummy variables. Eventually I want to take advantage of pgmm as well, but first I'd like to solve the problem with the non-dynamic version.

An example:

    load(url('http://andrewcd.berkeley.edu/sdat'))
    head(sdat)
    library(plm)
fem = plm(y~T+G:t,data=sdat,effect="twoways",model="within",index=c("ID","t"))
    summary(fem)
    lsdvm = lm(y~ID+T+G:t,data=sdat)
    summary(lsdvm)
    fem$coef

`fem` is the fixed-effects model (fit with plm), and `lsdv` is the equivalent least-squares dummy variable model (fit with lm)

It is clear that plm is estimating the coefficients, and indeed that the coefficients are identical in the two models, as they should be. But when I go to summarize the results, plm is having a hard time, and I'm pretty sure that the reason is the timeXgroup fixed effects, some of which need to be auto-omitted because of the dummy variable trap. (lm, for example, seems to know how to automatically remove variables that are exact linear combinations of each other).

How do I get around this? I'd prefer to stay with plm, as it gives much more parsimonious output than lm with dummy variables for each cross-sectional unit.

Thanks in advance for any help!

Best,
Andrew

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Reply via email to