In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization – explicit or implicit – embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found that traditional factors should almost always be included as predictors and moving average rotations of the data can provide important gains for various forecasting targets. Also, we note that while predicting directly the average growth rate is equivalent to averaging separate horizon forecasts when using OLS-based techniques, the latter can substantially improve on the former when regularization and/or nonparametric nonlinearities are involved.

在低维线性回归设置中，考虑预测变量的线性变换/组合不会改变预测。但是，当预测技术使用收缩或非线性时，它确实如此。这正是机器学习 (ML) 宏观经济预测环境的结构。数据的预处理转化为嵌入 ML 算法的正则化（显式或隐式）的改变。我们回顾旧的转换并提出新的转换，然后在大量的伪样本练习中凭经验评估它们的优点。研究发现，传统因素几乎总是应作为预测因素包括在内，并且数据的移动平均旋转可以为各种预测目标提供重要的收益。还，