Hi all,

 

I've been struggling with multicollinearity in a time series data set.
My 2 predictors are collinear because there is a biennial pattern in the
data (masting shrubs), and one of the predictors is just the response
variable (berry production) at t-1, and the other (a measure of soil
moisture) is functionally related to the response at time t.  I'm
tempted to proceed with a linear regression model, present the partial
regression coefficients, and argue that the effect of collinearity would
be to reduce power to identify significant effects of either predictor,
but have also been working with ridge regression. I think I've managed
to estimate lambda using boot::cv.glm, and then estimate the penalized
regression coefficients using lm.ridge, but am uncertain how to get
effective degrees of freedom, t-, and p-values for those coefficients. 

 

Any advice would be appreciated.

 

Thanks,

Eric   

 


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