Generalized Least Squares (GLS) is a statistical method used to estimate the parameters of a linear regression model when there is a possibility of heteroskedasticity or autocorrelation in the error terms. This technique improves efficiency by providing better estimates than Ordinary Least Squares (OLS) when the assumptions of OLS are violated, especially regarding constant variance and independence of errors. The GLS method essentially transforms the data to mitigate these issues, leading to more reliable statistical inference.
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