I had a time series model with 5 time series variables and it's a model that's reputed in the literature for having autocorrelation problems. Why when I use standard OLS var-covar matrix only 2 variables are (highly) significant but with Newey West varcovar matrix 4 of the variables are (highly) significant (1% level)?
There may be several effects. First, you may be hitting negative autocorrelations somewhere down the road. Second, most sandwich-like estimators are biased down, so with short samples (say of total length 10-15), you may have notably larger biases with the smaller data set.
If there are wild swings in significance, you need to proceed conservatively: report both, but act only on the evidence that's supported in both specifications.