In a time-series OLS regression of daily stock returns on a set of explanatory variables I want to test whether the error terms are auto-correlated before I decide to use Newey-West standard errors (heteroskedasticity is present).
Using the Breusch-Godfrey test, I have to specify a number of lags that shall be considered in the test for serial correlation.
- Is there any intuition (maybe an economic one?) for considering more than first order serial correlation?
- If yes, is there any theory or empirical finding that can be used to choose a reasonable lag length up to which I have to check?
Thank you very much for your help!!