--- On Tue, 20/10/09, [email protected] <[email protected]> wrote:
> Actually i would like to come up with a linear demand
> function like that:
> Consumption= constant+ beta_1*Price+beta_2*Income+ ...
> and a loglog demand function.
> I used the spreadheet programm and manually calculated
> Consumption for various prices and got the two demand
> functions from that, but unfortunatly, due to the log
> transformation i have the following problem:
> exp(E(ln(Consumption))) is not equal to E(Consumption).
> I thought more precise estimates would help-but as it
> turned out now- they don't.
> So, can I estimate the consumption for different
> prices directly in Stata to get the demand functions?
> Then, I should be able to deal with the retransformation
> bias as suggested in
> Cameron&Trivedi(2009):Microeconometrics using Stata.
You have two options:
1) Use -glm- with a log link function, and after that
you can just use -predict- to get the predicted values.
So you would type something like:
glm consumption price income ... , link(log)
predict consumptionhat, mu
2) Alternatively the method proposed by Cameron &
Trivendi is implemented in Stata by Kit Baum as
the -levpredict- package. You can install that
by typing in Stata: -ssc install levpredict-.
IMHO the -glm- solution is more elegant as it tackles
the problem at its root: In this case -glm- models
log(E(consumption)) while -regress- models
E(log(consumption)). Because of this the exponentiating
the left hand side and right hand side after a -glm-
model (which happens when you use -predict-, -adjust-,
or specify the -eform- option) will lead to results
in the original unit of consumption, where this is not
true after -regress-. The solution offered by Cameron
& Trivendi seems to me a bit of a post hoc solution.
-- Maarten
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl
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