I was wondering what the equivalent assumptions to Gauss Markov are for time series regression. Some are clearly different. Stationarity is not (as far as I know) an assumption of regression although it may be implicit in the assumption of equal error variance. Similarly time series normally does not assume that error terms are not serially correlated (they usually will be so you have to address that). And variables in time series are not iid until a significant period of time occurs in many cases.

I could be wrong on all of that So what are the time series assumptions other than Stationarity and how do you test for them?

In particular I am unclear if linearity and homoscedacity is part of the assumptions of multivariate time series.

I could be wrong on all of that So what are the time series assumptions other than Stationarity and how do you test for them?

In particular I am unclear if linearity and homoscedacity is part of the assumptions of multivariate time series.

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