LGB+: A Macroeconomic Forecasting Road Test

TL;DR

LGB+ is a novel boosting method combining linear and tree models, improving macroeconomic forecasts by capturing both linear and nonlinear dynamics effectively.

Contribution

It introduces a flexible boosting procedure that integrates linear and tree-based predictors without fixed assumptions, enhancing macroeconomic forecasting accuracy.

Findings

LGB+ outperforms traditional models in macroeconomic forecasting tasks.

The method effectively decomposes forecasts into linear and nonlinear components.

Variables with strong autoregressive signals benefit most from LGB+.

Abstract

Needless to say, linear dynamics are pervasive in economic time series, particularly autoregressive ones. While gradient boosting with trees excels at capturing nonlinearities, it is inefficient in small samples when much of the predictive content is linear, expending splits to approximate relationships better captured by simple linear terms. This paper proposes LGB+, a boosting procedure operating on a more inclusive set of basis functions. The idea comes in two flavors. LGB+ evaluates a tree and a linear candidate at each step against out-of-bag data; only the winner advances. The simpler variant, LGB^A+, alternates on a fixed schedule: a block of tree updates, then a greedy linear correction, repeat. Both designs avoid ex ante commitments to any particular functional form or predictor selection. Because the prediction is the sum of a linear and a tree component, forecasts decompose natively into linear and nonlinear contributions, and so does permutation-based variable importance and historical proximity weights. In a quarterly U.S. macroeconomic forecasting exercise, LGB+ delivers strong gains for targets with pronounced autoregressive dynamics or mixed linear-nonlinear signals. Variables dominating the linear channel are those operating through autoregressive persistence or near-accounting relationships to the target (e.g., initial claims for unemployment and building permits for housing starts).