向量自回归 (VAR) 及其多种变体是经济和金融研究中的标准模型，因为它们具有预测、数据分析和推理的能力。这些属性是它们包含多个变量和滞后的能力的结果，然而，这会变成要估计的参数的指数增长。这意味着具有多个变量和滞后的高维模型难以估计，导致遗漏变量、信息偏差和潜在预测能力的丧失。传统上，现有文献采用因子分析，特别是贝叶斯方法来克服这种情况。本文探讨了所谓的机器学习正则化方法，以替代传统的预测和脉冲响应分析方法。我们发现允许高维模型的正则化结构在临近预报和预测方面比标准贝叶斯方法表现更好。此外，脉冲响应分析是稳健的，并且与经济理论和证据以及不同的正则化结构一致。具体来说，关于最佳正则化结构，元素机器学习结构在临近预报和计算效率方面表现更好，而组件结构在预测和交叉验证方法中表现更好。 Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods.