Hi all,

I am running an ARDL model in the form:

Code:
 reg y l.y x1 l.x1 x2 l.x2
After the estimation I check for serial correlation with both the 'estat durbinalt' and 'estat bgodgrey' postestimation commands for lags from 1 to 3. I do not find serial correlation at lag 1 and 2, but I do at lag 3. So I re-estimate the model using:

Code:
 newey y l.y x1 l.x1 x2 l.x2, lag(3)
My question is: why I get smaller standar errors (and coefficient breaches the significance level) with the HAC s.e.? Should not they be more conservative in order to correct for serial correlation and heteroskedasticity?

On a different line, assuming there is no serial correlation (so for instance at lag 1 or 2 and I do not run a test for lag 3), why would 'newey' lead to different s.e. than 'regress, robust'?

Thank you very much for your help. Best,

Michele