Hi, from an output of regression results… what factors do we look for to determine if it’s covariance stationary?

Do we look at the regression coefficients/t-stat or the autocorrelation information or the first differenced correlation/coefficient results?

thanks

If it’s an AR(1) model, |*b*_{1}| must be less than 1 for it to be covariance stationary.

That’s the only model mentioned in the CFA curriculum.

Dickey-Fuller test. I don’t believe you can infer covariance stationary just by looking at a generic regression output.

The Dickey-Fuller test is a test for a unit root. You would use it if *b*_{1} is close to 1. It won’t necessarily tell you whether or not the regression is covariance stationary.

Does the autocorrelation results of the residual for AR(1) infer imply anything? (or is it just for detecting serial correlation?)

thanks

i believe it’s only for detecting serial correlation which would imply the accuracy of the model