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In ordinary linear regression, Y = X$\beta$ + $\epsilon$, if the error is autocorrelated, then the assumptions under the Gauss-Markov theorem are violated. For example, autocorrelation violates the independence assumption. As a result, the LS estimator $\hat{\beta}$ is not BLUE any more. explanation

I think LASSO does not take into consideration of autocorrelation effects because of its objective function for the model Y = X$\beta$ + $\epsilon$. Then my question is what consequences of ignoring autocorrelation when I am applying LASSO to obtain the LASSO estimator?

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