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