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Re: st: Negative LR test statistic ?


From   Ekrem Kalkan <kalkan.ekrem@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Negative LR test statistic ?
Date   Tue, 22 Dec 2009 20:21:13 +0200

Thank you Peter, and merry Christmas.

2009/12/22 Lachenbruch, Peter <Peter.Lachenbruch@oregonstate.edu>:
> 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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis
> Sent: Monday, December 21, 2009 3:08 PM
> To: statalist@hsphsun2.harvard.edu
> 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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