Take the 2-minute tour ×
Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. It's 100% free, no registration required.

If we have a time series of returns and two time series of indicators, how would we test the use of these indicators if they are autocorrelated or nonstationary (VAR Models dont produce significant results).

share|improve this question
1  
What I mean to say is I want to test whether a signal can predict returns, but the signal is autocorrelated and possibly nonstationary. How would I go about this testing? –  user7524 Mar 11 at 21:27

1 Answer 1

The classical assumptions of linear regression are that the errors are uncorrelated and the variance of errors is constant (homoskedastic). So regress the returns against the indicators and test for autocorrelation and heteroskedasticity in the errors. If you don't observe any, then there's no issue with conventional hypothesis testing. If you do, use White or Newey-West standard errors (standard in most statistical packages), as appropriate, to compute new t-statistics, then proceed with hypothesis testing.

share|improve this answer

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.