multicollinearity

A problem in regression analysis is multicollinearity, which is to say moderate or high correlations among the independent variables.

Multicollinearity and Micronumerosity, Bryan Caplan | EconLog | Library of Economics and Liberty 2009

But if we had to use historical data instead, we would sove the problem of multicollinearity by using factor analysis or partial least squares, both of which combine the data into fewer, but independent predictors.

Multicollinearity and Micronumerosity, Bryan Caplan | EconLog | Library of Economics and Liberty 2009

Goldberger's main point: People who use statistics often talk as if multicollinearity high correlations between independent variables biases results.

Multicollinearity and Micronumerosity, Bryan Caplan | EconLog | Library of Economics and Liberty 2009

In the context of an inverse regression, you have to think long and hard about whether a procedure for regression of effect upon causes (tree ring ~ temperature + precipitation) where you want orthogonality can be transmogrified into an inverse regression of cause upon effect in the style of dendroclimatologists ( temperature ~ bristlecones+ Gasp + …), where you actually want multicollinearity (i.e. a signal).

More on "Naturally Orthogonal" « Climate Audit 2007

And what about the common trend in CO2 and Solar, no multicollinearity problems? jae

IPCC and Solar Correlations « Climate Audit 2007

Evidence of multicollinearity is consistent with that hypothesis and certainly not a “problem” for it.

Groveman and Landsberg « Climate Audit 2006

In short, whether or not multicollinearity is a problem depends on what hypothesis you are trying to test.

Groveman and Landsberg « Climate Audit 2006

In theory, PLS is applied in situations of multicollinearity, but the MBH network has many series that are essentially white noise and thus the proxies are surprisingly close to being orthogonal in the early networks and there are blocks of orthogonal series in the later network.

More on Bürger et al 2006 « Climate Audit 2006

For all you wild econometric fans: we are selecting on high multicollinearity in the Y to Z relationship.

Esper on In-Site Cherry Picking « Climate Audit 2006

In my experience, interaction variables are kitchen sink type regressors that induce severe multicollinearity and give spurious results.

Archive 2005-11-27 Steve Sailer 2005