A general rule of thumb is that the number of observations should be about 10 times the number of predictor variables for a single linear regression - it's not absolute, but seems to hold fairly well. Thus, with 14 equations, you would probably not want to have much more than 5 or 6 predictors.
Happy holidays all,
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Maarten buis
Sent: Monday, December 21, 2009 3:08 PM
To: [email protected]
Subject: Re: st: Negative LR test statistic ?
> --- On Mon, 21/12/09, Ekrem Kalkan wrote:
> > I am estimating a system of 14 equations, each with
> > nearly 40 variables. I have also 20 excluded instruments. What
> > do you mean by "empty"model? If you mean the model without
> > explanatory variables, there will be only 14 constant term to
> > be estimated. Is it too large?
--- I answered:
> I am afraid that this could very well be the case. Think of it
> this way: you have only a bit more than 60 observations per
> equation. 60 is OK but not great for linear regression, as it
> is known to be robust, well behaved, and stable, but your are
> realy pushing your luck when using such small sample sizes for
> anything more complicated. This is especially true for anything
> involving instrumental variables, these models can easily eat
> huge amounts of statistical power.
Let me add to that: 40 covariates would be way too much with only
60 observations, even for a linear regression. What you could do
to get a feel for how much your data can take, is to do a power
analysis as described here:
http://www.stata.com/support/faqs/stat/power.html
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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