I understand the basic difference in definition between multicollinearity and autocorrelation. I.e multicollinearity describes a linear relationship between whereas autocorrelation describes correlation of a variable with itself given a time lag.
When should I test for these as part of hypotheses testing? When fitting a model to a time series are the error terms tested for autocorrelation or multicollinearity? Why one over the other?
In a linear regression between Y and X with no time component, I suppose the answer is easy? We fit a linear model and test the residuals for multicollinearity and not autocorrelation because we are not considering time as a factor here. I am sorry for such a naive question.