[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Negative LR test statistic ? |

Date |
Tue, 22 Dec 2009 09:00:01 -0800 |

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 -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Negative LR test statistic ?***From:*Ekrem Kalkan <kalkan.ekrem@gmail.com>

**References**:**Re: st: Negative LR test statistic ?***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Negative LR test statistic ?***From:*Maarten buis <maartenbuis@yahoo.co.uk>

- Prev by Date:
**AW: RE: st: RE: Panel data. Multiple variables generation.** - Next by Date:
**Re: AW: st: RE: Graphing time on the x axis** - Previous by thread:
**Re: st: Negative LR test statistic ?** - Next by thread:
**Re: st: Negative LR test statistic ?** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |