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.

翻译：我们通过系统整合三个关键原则来预测宏观经济结果的完整条件分布：使用高维数据并辅以适当正则化、采用严格的样本外验证程序，以及纳入非线性因素。通过挖掘大量宏观经济与金融预测变量中蕴含的丰富信息，我们实现了对宏观经济风险全貌的实时精准预测。研究结果表明：通过收缩实现的正则化对于控制模型复杂度至关重要，而引入非线性因素对预测精度的提升效果有限。样本外验证在模型架构选择与防止过拟合方面发挥着关键作用。