I understand that heteroscedasticity leads to problems with coefficient estimates of a model, but I'd like to know how it relates to predictive accuracy. After creating my original linear model, I am able to model the magnitude of the residuals (R^2~.06) using the variables from my original model. I tried Weighted least squares, however can't get anything to improve the predictive accuracy of my original model. I am only concerned with predictive accuracy and so would like to know if there is anything else I could try and also if the existence of heteroskasdicity implies that predictions could definitely be improved.