I am trying to determine the optimal lag order in a two-equation VAR as follows:
- choose the lag order based on information criteria;
- estimate the model using # of lags determined above and test for autocorrelation in errors (up to order 4): if auctocorrelation is found at any of the orders I add one additional lag and test again (lags are added until autocorrelation disappears).
Is this approach sensible? Also, given that I have limited sample size (around 150 observations), what is the maximum number of lags I should allow?
The goal is to use the model for testing Granger causality using the Toda-Yamamoto procedure.