[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
RE: st: RE: Multicollinerity test in IV regression |

Date |
Wed, 13 Oct 2004 22:38:36 +0100 |

Excellent advice. I did say "often", not always. Nick n.j.cox@durham.ac.uk > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Marcello > Pagano > Sent: 13 October 2004 22:34 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: RE: Multicollinerity test in IV regression > > > I agree somewhat with what Nick says, but I would like to take > it one step further. Indeed, mathematically one can think of the > predictors, over this particular sample region, as defining a linear > space. If the space is not of full rank, then there is > multicollinearity. > That means that a linear combination of two, or more of the > predictor variables, or even linear combinations of two or more of > the predictor variables, equal zero. > > In the finite precision world we live in with computers, zero is > interpreted to be relatively small. This is especially important > in the statistical world we live in where the predictors themselves > (big secret) may be known with error. > > These linear combinations are not unique, but, and here is where > I disagree with Nick, they are informative. Possibly because it is > not clear that the solution is the simple one implied by Nick: > drop one, or more, variables until we get rid of the problem. > A better solution might be to replace all offending predictors > by (a) linear combination(s) that make sense. For example if > X1+X2=0, then drop both X1 and X2 and replace them with the > new variable X1+X2. You can extend this to the more general > situation. > > > Just a thought. > > m.p. > > > > Nick Cox wrote: > > >I assert that multicollinearity is a property of the > >predictors and does not depend on what > >you do with them before, during or > >after any examination of multicollinearity. > > > >You can look at the structure of relationships > >with -graph matrix-; get numerical summaries > >by using -correlate-; and use -_rmcoll- to > >look further. > > > >Whether there is some omnibus test of multicollinearity > >I do not know. If there were, it wouldn't necessarily > >be helpful in indicating what to do. > > > >I have always found it most useful > >to think about the meanings of variables and the > >roles they play in terms of the underlying science. > >That is, reflection often makes it seem unsurprising > >that two or more variables are highly correlated, > >so that they tell the same story and only one > >need be recorded. > > > >However, there are other issues particularly where > >lots of dummies are included. > > > >Nick > >n.j.cox@durham.ac.uk > > > >Rozilee Asid > > > > > > > >>Just to ask one simple question, how do I test for the existence of > >>multicollinearity after using ivreg2 command. > >> > >> > >> > > > >* > >* For searches and help try: > >* http://www.stata.com/support/faqs/res/findit.html > >* http://www.stata.com/support/statalist/faq > >* http://www.ats.ucla.edu/stat/stata/ > > > > > > -- > ______________________________________________________________________ > > Marcello Pagano > Biostatistics Department Tel: 1-617-432-4911 > Harvard School of Public Health Fax: > 1-617-432-5619 > 655 Huntington Avenue > email:pagano@biostat.harvard.edu > Boston, MA 02115 http://biosun1.harvard.edu/~bio200 USA eppur si muove * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**Re: st: RE: Multicollinerity test in IV regression** - Next by Date:
**RE: st: RE: Multicollinerity test in IV regression** - Previous by thread:
**Re: st: RE: Multicollinerity test in IV regression** - Next by thread:
**RE: st: RE: Multicollinerity test in IV regression** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |