Is it ever better to use ordinary least squares (OLS) over weighted least squares (WLS)? If a model is fit well by OLS, will I get worse results if I use WLS instead?
Obviously, OLS is faster, but who cares if it takes an extra 5 minutes.
If the variance of the errors from a sarmax model evident a determinstic change at one or more points, this can't be easily ignored and needs to be rectified thus Weighted Least Squares a particular form of GLS is in order..as was discussed in Is this method to make data approximatly stationary valid?
Worst in what sense ? hypothesis testing ? prediction limits ? mape computation for a specific # of withheld values ? It would all depend !