  Random forest regression (RF) is an extremely popular tool for the analysis
of high-dimensional data. Nonetheless, its benefits may be lessened in sparse
settings due to weak predictors, and a pre-estimation dimension reduction
(targeting) step is required. We show that proper targeting controls the
probability of placing splits along strong predictors, thus providing an
important complement to RF's feature sampling. This is supported by simulations
using representative finite samples. Moreover, we quantify the immediate gain
from targeting in terms of increased strength of individual trees.
Macroeconomic and financial applications show that the bias-variance trade-off
implied by targeting, due to increased correlation among trees in the forest,
is balanced at a medium degree of targeting, selecting the best 10--30\% of
commonly applied predictors. Improvements in predictive accuracy of targeted RF
relative to ordinary RF are considerable, up to 12-13\%, occurring both in
recessions and expansions, particularly at long horizons.
