New method corrects data errors, improving accuracy in statistical modeling.
Researchers developed a method to estimate and test stochastic variance models by transforming them into a linear state space form. They found that by correcting observations for heteroscedasticity, models with explanatory variables can be handled effectively. The approach is robust as it does not require specifying distributions for disturbances.