Ridge regularized estimation of VAR models for inference
研究了岭回归在向量自回归模型估计和推断中的应用，分析了各向异性惩罚的影响，并与贝叶斯岭估计器比较，通过蒙特卡洛模拟评估了脉冲响应函数的估计效果。
Ridge regression is a popular method for dense least squares regularization. In this article, 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.