The random forest regression (RF) has become an extremely popular tool to analyze high-dimensional data. Nonetheless, it has been argued that its benefits are lessened in sparse high-dimensional settingsdue to the presence of weak predictorsand an initial dimension reduction (targeting) step prior to estimation is required. We show theoretically that , in high-dimensional settings with limited signal, proper targeting is an important complement to RF's feature sampling by controlling the probability of placing splits along strong predictors . This is supported by simulations with representable finite samples. Moreover, we quantify the immediate gain from targeting in terms of increased strength of individual trees. Our conclusions are elaborated by a broad set of applications within macroeconomics and finance. These show that the inherent bias-variance trade-off implied by targeting, due to increased tree correlation , is balanced at a medium level , selecting the best 10-30\\%DIF < of commonly applied predictors. The applications consolidate that improvements from the targeted RF over the ordinary RF can be significant, particularly in long-horizon forecasting, and both in expansions and recessions.\end{abstract} %DIF > 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.
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 tradeoff 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\\%DIF < of commonly applied predictors. The applications consolidate that improvements from the targeted RF over the ordinary RF can be significant, particularly in long-horizon forecasting, and both in expansions and recessions.\end{abstract} %DIF > 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.
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