Map of Forrest on If errors have differing volatility (known as heteroscedasticity) then some observations are more precise than others. OLS does not use this information. While it is still unbiased, OLS is not efficient in the presence of heteroscedasticity. If errors are correlated, as is common with time-series data (known as autocorrelation), one time period can help predict another time period. With both heteroscedasticity and autocorrelation, Weighted Least Squares provides a more efficient estimate by using the additional information to weight the sum of squared errors. 5. There must be more data points than parameters to be estimated. Map of Forrest 2016.
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