Dealing up with collinearity predictors' choice in xreg using auto.arima

by Gianmarco Di Muro   Last Updated February 11, 2019 09:19 AM

I'm trying to do a regression with arima errors in R, with xreg in auto.arima following https://otexts.com/fpp2/ by https://robjhyndman.com/ but I have some questions about the predictors' choice in xreg in auto.arima.

1) What should be done if the predictors x1,x2,x3 are higly correlated in xreg=cbind(x1,x2,x3)?

Is there a problem if the columns of matrix are highly correlated like in linear regression?

2) If 1) is yes, is there any automatic procedure to select no-multicollinearity predictors in auto.arima? Or auto.arima already do this when you launch it? Or i have to study multicollinearity of columns of matrix previously?

3) For forecasting, i have read that it doesn't matter if the predictors in auto.arima() are not significant. Is it right?

Thank you for the help.



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