dummies and multicollinearity in lagsarlm

Hello,

is there a way to estimate a sar model (> lagsarlm) with a dummy variable in
order to discriminate into two spatial regimes and obtain two different
coefficients' estimations for each variable? So far I've been constructing
two new variables from each original one by multiplicating it by each dummy,
that is: GPC*D1 and GDP*D2. After that I've been running a regression by
using the two new variables as explanatory v.
Since the two dummies are perfecly complementary, I guess there might be a
problem of multicollinearity in this way of estimating the model. Is there
an option like the "subset" one in the "lm" function which I can use in a
SAR model? Or otherwise, how would you estimate a SAR model with two spatial
regimes in R?

Thank you
Annachiara Saguatti

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Since I want to obtain a common spatial autoregressive parameter (rho) is it
correct to estimate

lagsarlm(formula= y ~ D1 / (x1+x2+x3) + D2 / (x1+x2+x3) - 1, data=...,

listw=...)

where D1 and D2 are two complementary dummies?
Estimating a model with just one of the two returns me the coefficient for
only one of the two groups of regions, right?

Thanks,
Annachiara


2008/12/18 Roger Bivand <Roger.Bivand at nhh.no>

On Thu, 18 Dec 2008, Annachiara Saguatti wrote:

 Hello,

is there a way to estimate a sar model (> lagsarlm) with a dummy variable
in
order to discriminate into two spatial regimes and obtain two different
coefficients' estimations for each variable? So far I've been constructing
two new variables from each original one by multiplicating it by each
dummy,
that is: GPC*D1 and GDP*D2. After that I've been running a regression by
using the two new variables as explanatory v.
Since the two dummies are perfecly complementary, I guess there might be a
problem of multicollinearity in this way of estimating the model. Is there
an option like the "subset" one in the "lm" function which I can use in a
SAR model? Or otherwise, how would you estimate a SAR model with two
spatial
regimes in R?

This is one of the strengths of the formula abstraction. Dummies are not
needed, just a factor. The notation would be something like:

formula=y ~ factor / (x1 + x2 + x3 + ...) - 1, data=...

with / using the factor to fit coefficients of the xi for each level of
factor, and -1 removing a global intercept, so giving an intercept for each
level of factor. The Chow test is then anova() of the model without factor /
... -1 against the model with it.

Hope this helps,

Roger

Thank you
Annachiara Saguatti

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--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no