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.

翻译：脊回归是一种流行的密集最小二乘正则化方法。本文在向量自回归模型估计与推断的框架下研究脊回归，探讨各向异性惩罚的影响，并与贝叶斯脊型估计量进行比较。本文分析了渐近分布及交叉验证技术的性质。最后，通过蒙特卡洛模拟评估脉冲响应函数的估计，并将脊回归与多种相似及竞争方法进行比较。