Ridge regularized estimation of VAR models for inference

IF 1.2 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Time Series Analysis Pub Date : 2024-02-18 DOI:10.1111/jtsa.12737

Giovanni Ballarin

引用次数: 0

Abstract

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.

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用于推理的 VAR 模型的岭正则化估计

脊回归是一种常用的密集最小二乘法正则化方法。本文结合 VAR 模型的估计和推断对岭回归进行了研究。文章讨论了各向异性惩罚的影响，并与贝叶斯脊型估计器进行了比较。分析了交叉验证技术的渐近分布和特性。最后，通过蒙特卡罗模拟对脉冲响应函数的估计进行了评估，并将脊回归与一些类似的竞争方法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Time Series Analysis 数学-数学跨学科应用

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.