Title: Forecasting Near-equivalence of Linear Dimension Reduction Methods
in Large Panels of Macro-variables

In an extensive pseudo out-of-sample horserace, classical estimators (dynamic
factor models, RIDGE and partial least squares regression) and the novel to
forecasting Sliced Inverse Regression exhibit almost near-equivalent forecasting
accuracy in a large panel of macroeconomic variables across targets, horizons
and subsamples. This finding motivates our theoretical contributions in this
paper. We show that most widely used linear dimension reduction methods solve
closely related maximization problems with solutions that can be decomposed in
signal and scaling components. We organize them under a common scheme that sheds
light on their commonalities and differences as well as on their functionality.
Sliced Inverse Regression delivers the most parsimonious forecast model and
obtains the most dramatic reduction of the complexity of the forecasting problem.