Generalized least squares (GLS) is a statistical technique used to estimate the parameters of a regression model when there is a possibility of heteroscedasticity or when the residuals are correlated. This method modifies the ordinary least squares (OLS) approach by incorporating a weighting scheme to provide more accurate parameter estimates. By adjusting for the structure of the error variance or correlation, GLS improves the efficiency of the estimates and reduces bias in the results, making it a powerful alternative to OLS in certain situations.
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