Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

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

From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Can multicollinearity problems be resolved by using residuals from another regression? |

Date |
Mon, 26 Nov 2012 19:46:50 -0500 |

Sorry for coming late to this topic. I wanted to point out that ridge regression, a family of methods for remediating multicollinearity, is implemented by -ridgereg- (SSC). For background, see the following reference, listed in the -help-: Evagelia, Mitsaki (2011) "Ridge Regression Analysis of Collinear Data", It can be downloaded from: http://www.stat-athens.aueb.gr/~jpan/diatrives/Mitsaki/chapter2.pdf Steve On Sat, Nov 10, 2012 at 12:16 PM, JVerkuilen (Gmail) <jvverkuilen@gmail.com> wrote: > On Thu, Nov 8, 2012 at 9:36 PM, A. Shaul <3c5171@gmail.com> wrote: >> Dear Statalist, >> >> I expect a non-linear effect of an exogenous variable, x1, on a >> dependent variable, y. The variable x1 is affected by another >> exogenous variable, x2. The variable x2 affects x1 directly and also y >> directly. The variable x1 does not affect x2. I am only interested in >> the partial effect of x1 on y while controlling for x2 --- or at least >> while controlling for the part of the variation in x2 that affects y >> directly. >> >> I have the following regression equation: >> >> (1) y = b1*x1 + b2*(x1)^2 + b3*x2 + constant > > I'm not 100% sure what you're doing but when you have polynomial terms > like this collinearity is inevitable. Before doing anything odd, > center x1 and then compute x1^2, and regress on the centered > variables. (You may want to rescale x1 as well but centering does the > work.) This will give you a statistically equivalent model that breaks > the collinearity between x1 and x1^2. > > Usually though you're not interpreting x1 terms directly anyhow, so > whether x1 or x1^2 is statistically significant individually is > irrelevant. Certainly the linear term for x1 is irrelevant if the term > for x1^2 is significant. You can test for x1 effects as a block using > -testparm-. > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Can multicollinearity problems be resolved by using residuals from another regression?***From:*"A. Shaul" <3c5171@gmail.com>

**Re: st: Can multicollinearity problems be resolved by using residuals from another regression?***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

- Prev by Date:
**st: R: st: Re: st: "cluster(firm)” vs “vce(cluster firm)”** - Next by Date:
**Re: st: Can multicollinearity problems be resolved by using residuals from another regression?** - Previous by thread:
**Re: st: Can multicollinearity problems be resolved by using residuals from another regression?** - Next by thread:
**Re: st: Can multicollinearity problems be resolved by using residuals from another regression?** - Index(es):