标题： 宏观经济预测与机器学习 显示英文标题

标题： Macroeconomic Forecasting and Machine Learning

摘要： 我们通过系统地整合三个关键原则，预测宏观经济结果的完整条件分布：使用具有适当正则化的高维数据，采用严格的样本外验证程序，并包含非线性关系。 通过利用大量宏观经济和金融预测因子中嵌入的丰富信息，我们实时生成宏观经济风险整个轮廓的准确预测。 我们的研究结果表明，通过收缩进行正则化对于控制模型复杂性至关重要，而引入非线性关系对预测准确性提升有限。 样本外验证在选择模型结构和防止过拟合方面起着关键作用。 显示英文摘要

摘要： We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regularization via shrinkage is essential to control model complexity, while introducing nonlinearities yields limited improvements in predictive accuracy. Out-of-sample validation plays a critical role in selecting model architecture and preventing overfitting.