Comment 2 for bug 428911

The prediction error is the sum of the error variance plus the variance that comes from the uncertainty in the parameter (beta) estimates

A version that works with independently distributed case, OLS and WLS, but not for general GLS

http://bazaar.launchpad.net/~josef-pktd/statsmodels/statsmodels-josef-experimental/annotate/head%3A/scikits/statsmodels/sandbox/regression/predstd.py

I'm not sure whether we should attach it to results or rewrite so it can be attached to the model (WLS)

I'm waiting for a GLS case, random effects model, not sure about GLSAR, to see whether predict should be in model or result class. I suspect that once we have other classes than OLS, GLS then we need model specific prediction code.

For GLSAR, I would introduce new method in model called "forecast", distinction between forecast and predict as in Greene, with or without knowledge of time.

GLS for arbitrary correlation is still unclear, eg. spatial econometrics and Gaussian Processes with kernel covariance structure.