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

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 > -------------------------- > > > > > * > * 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/ > * * 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/

**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>

**RE: st: Negative LR test statistic ?***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

- Prev by Date:
**st: RE: RE: RE: PCA and rotation** - Next by Date:
**st: RE: RE: RE: RE: PCA and rotation** - Previous by thread:
**RE: st: Negative LR test statistic ?** - Next by thread:
**Re: st: Negative LR test statistic ?** - Index(es):

© Copyright 1996–2014 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |