  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.
