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Ridge regression is a popular method for dense least squares regularization.In this work, ridge regression is studied in the context of VAR modelestimation and inference. The implications of anisotropic penalization arediscussed and a comparison is made with Bayesian ridge-type estimators. Theasymptotic distribution and the properties of cross-validation techniques areanalyzed. Finally, the estimation of impulse response functions is evaluatedwith Monte Carlo simulations and ridge regression is compared with a number ofsimilar and competing methods.

Ridge Regularized Estimation of VAR Models for Inference