Kit,
Thanks very much for replying. Just a few notes:
> If region is a categorical variable, and these are xt data, then there are
> two possibilities: region modifies the constant term (in which some sort
> of fe or re model should be used) or region modifies the entire
> relationship (including the coeff on midch). In the latter case a set of
> interacted dummies would be used in a fe context, or one could use some
> sort of random-coefficients model (Hildreth-Houck).
Of course, I used REGION as an example. In terms of continuous 'third'
covariates, does the method change? I've been using OLS (when the
Gauss-Markov assumptions have been satisfied) or FGLS up until now. Most
of the explantory variables in my models (i.e., net turnout rates and
party competition) are continuous.
> I did not respond to the original enquiry since the answer seemed obvious:
> if there is a third variable that (one suspects) should be in the
> relationship, and it is measurable, the correct methodology is to include
> it. After having done so, one may test for its relevance. Techniques such
> as dealing with proxy issues would only arise if the variable in question
> is not quantifiable.
I want to shriek my reply to this, but I'll simply say "I agree with all
of the above!" That's what I've been doing all along. It was a critical
query of part of my work that that brought on doubts that I was modelling
my variables of interest in the correct way.
CLIVE NICHOLAS |t: 0(044)191 222 5969
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
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