Giovanni,

Thank you for your reply. pvcm is indeed what I was looking for.

I have a follow-up questio: plm documentation says that pvcm with
method 'within' is equivalent of estimating a separate model for each
individual. Let's assume that I would like to estimate a panel data
model with an individual-specific intercept, individual-specific slope
and a regressor that has a common coefficient for all individuals.
I.e. a model of the form:  y_it = a_i + b_i*t + c*x_it + e_it. Is this
doable in plm?

I came up with two non-plm solutions to my problem. One possible
approach would be to estimate a model y ~ id + id*t + c with ols, but
this doesn't seem feasible with a dataset of several hundreds of
individuals. Another approach would be to apply Frisch-Waugh-Lovell
theorem and estimate the regression coefficients in multiple steps.
Using plm, if possible, would feel more straightforward though.

Regards,
Otto


On Thu, Feb 4, 2010 at 4:46 PM, Millo Giovanni
<giovanni_mi...@generali.com> wrote:
> Dear Otto,
>
> please see ?pvcm and the section on variable coefficient models here
> http://www.jstatsoft.org/v27/i02/paper
>
> Should suit your needs; else please let me know.
>
> Best,
> Giovanni
>
> ------------- original message -----------------
>
> Message: 29
> Date: Wed, 3 Feb 2010 16:27:45 +0200
> From: Otto K?ssi <otto.ka...@gmail.com>
> To: r-help@r-project.org
> Subject: [R] Package plm & heterogenous slopes
> Message-ID:
>        <bcc3d8561002030627i354db4e8i363d3b1c886f0...@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Dear r-helpers,
>
>  I am working with plm package. I am trying to fit a fixed effects (or
> a 'within') model of the form
> y_it = a_i + b_i*t + e_it,  i.e. a model with an individual-specific
> intercept and an individual-
> specific slope.
>
> Does plm support this directly?
>
> Thanks in advance!
> Otto Kassi
>
>
>
> ------------- end original message -------------
>
> Giovanni Millo
> Research Dept.,
> Assicurazioni Generali SpA
> Via Machiavelli 4,
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> fax  +39 040 671160
>
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