![]() In cases where they differ substantially, the procedure can be iterated until estimated coefficients stabilize (often in no more than one or two iterations) this is called iteratively reweighted least squares. Weighted least squares estimates of the coefficients will usually be nearly the same as the "ordinary" unweighted estimates.The difficulty, in practice, is determining estimates of the error variances (or standard deviations).Some key points regarding weighted least squares are: We consider some examples of this approach in the next section. \(\begin^2\).Īfter using one of these methods to estimate the weights, \(w_i\), we then use these weights in estimating a weighted least squares regression model. The method of weighted least squares can be used when the ordinary least squares assumption of constant variance in the errors is violated (which is called heteroscedasticity). The method of ordinary least squares assumes that there is constant variance in the errors (which is called homoscedasticity). ![]()
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