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When the regressors of a econometric linear model are nonorthogonal, it is well known that their estimation by ordinary least squares can present various problems that discourage the use of this model. The ridge regression is the most commonly used alternative; however, its generalized version has hardly been analyzed. The present work addresses the estimation of this generalized version, as well as the calculation of its mean squared error, goodness of fit and bootstrap inference.
Gómez et al. (2024) studied this question.