Heteroskedasticity refers to a situation in regression analysis where the variability of the errors is not constant across all levels of the independent variable. This means that the spread or dispersion of the residuals varies at different values of the predictor variable, which can lead to inefficiencies in estimations and biased statistical tests. Recognizing and addressing heteroskedasticity is crucial when applying advanced forecasting techniques, as it affects the reliability of predictions and the validity of inferential statistics.
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