Pablo,
Quoting "Zoido, Pablo" <[email protected]>:
> Hi,
>
> I have a large dataset that contains purchases of different firms
> from
> different cities who buy from different kinds of providers at
> different
> times. Say I want to run a regressions on the order's prices on the
> order's
> quantities and I want to control for differences across time, cities
> and
> kinds of providers by including fixed effects for each of these
> categories.
> I have a large number of fixed effect for each city and a kind of
> provider.
>
>
> How can I run such a regression without necessarily asking stata
> to
> calculate and include in the regression all of the dummy variables
> for each
> category.
>
> If I use areg, or xtreg, fe I can only capture one fixed effect and
> the rest
> have to be included by brute force.
>
> Is there anyway around this? Can I create a variable say
> fe=group(city time
> provider) and then use this for my xtreg, fe i(fe)? Why not?
If I understand your problem correctly, then the answer is what you have
just suggested. You construct a variable fe that identifies uniquely the
combination of city, time, and provider. You then tell xtreg or areg that
this is your fixed effect. Should work fine.
--Mark
>
> Thank you in advance for your help.
>
> Pablo
>
> ps: I am sure that many people must have confronted this problem. I
> am not
> sure I am making myself very clear. Basically, I have a lot of fixed
> effects
> to include in a regression that come from different sources, some
> times I
> even need an interaction effect. The problem is I cannot use xi:
> because I
> have too many categories and dummies to create.
>
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: [email protected]
web: http://www.sml.hw.ac.uk/ecomes
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