Statalist


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

Re: st: Multicollinearity and Orthogonalization


From   "Erasmo Giambona" <e.giambona@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multicollinearity and Orthogonalization
Date   Wed, 20 Aug 2008 19:07:09 +0200

Thanks very much Marteen.
Erasmo

On 8/20/08, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> --- Erasmo Giambona <e.giambona@gmail.com> wrote:
> > One thing remains unclear to me. I thought (perhaps wrongly) that
> > one problem with multicollinearity is that if y1 and y2 are highly
> > correlated (e.g., > 0.9), then their coefficient estimates in
> > regression can get "artificially" alternate signs (e.g., + and - or
> > vice versa). To me is not clear yet whether you suggest that Stata
> > would not suffer from this problem or whether I should orthogonalize
> > in this case.
>
> If your coefficients are highly correlated then they will be measured
> with low precision, in other words there will be large differences in
> estimated coefficients across samples. This may mean that even the sign
> changes from sample to sample. However, this is not a problem, as this
> is exactly what the confidence interval is designed to warn you about.
> It will tell you that if you where to draw a new sample you could just
> as well find a coefficient that has the opposite sign as in your
> current sample. So like any other analysis you will have to look at
> both the point estimate and the confidence interval/standard error/
> p-value. As long as you do that, you will draw the correct conclusion
> from your data.
>
> I wouldn't put too much emphasis on the differences across packages,
> these differences are not in the statistics, but in the way they
> interact with the computer: A statistical technique involves
> computations, and while doing the computations a computer needs to
> store numbers, and you cannot store numbers with a infinite number of
> digits, so you will have to round while computing. You can do that
> smartly or less smartly, and when you do that less smartly your
> computations can go wrong. Very high multicolinearity can be situation
> where doing the computations less smartly can cause problems.
> Fortunately Stata does these computations smartly. Most other
> statistical packages are pretty good as well, and I have heard nothing
> about SAS that would make me particularly worried about that package.
> The packages I would really worry about are the ones that aren't
> specifically designed for statistical analysis, like Microsoft Excel.
>
> -- Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
> Send instant messages to your online friends http://uk.messenger.yahoo.com
> *
> *   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/



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