Macroeconomic Data Transformations Matter
This work addresses the issue of data pre-processing for economists and forecasters using ML, but it is incremental as it builds on existing transformations and empirical evaluations.
The paper tackles the problem of how data transformations affect macroeconomic forecasting when using machine learning methods with shrinkage or nonlinearities, finding that including traditional factors and using moving average rotations can provide important gains for various forecasting targets.
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.