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Re: st: Systematic Estimations


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Systematic Estimations
Date   Wed, 25 May 2011 15:05:37 +0200

On Wed, May 25, 2011 at 2:41 PM, Barbara Engels wrote:
> I have a less technical and rather general question.  I am a newbie regarding empirical evaluations of time-series. I am dealing with the relation between total factor productivity and research and development expenditure now. There are many variables that could play a role in determining total factor productivity. I have been trying to estimate regressions for quite some time, introducing variables, excluding them again. Estimated coefficients have changed dramatically in value and sign, and so did R^2.
> My question is: Is there any recommendable system of how to pick and drop variables again, making sure that THIS regression equation is better than the OTHER and not the other way around without getting lost in wild estimations?

That is a difficult problem. Some would say that you need to have a
prior theory and stick to whatever the data may say and should not
"snoop" around in your data. There is some truth in that, but often
that is just not a practical solution. A nice alternative is discussed
in: Edward E. Leamer (1983) Let's Take the Con Out of Econometrics.
The American Economic Review, Vol. 73, No. 1 (Mar., 1983), pp. 31-43.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------

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