Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil

• Published in: Computational economics 2024-07, Vol.64 (1), p.377-409
• Main Authors: Schultz Lindenmeyer, Guilherme, da Silva Torrent, Hudson
• Format: Article
• Language: English
• Subjects: Averages; Behavioral/Experimental Economics; Boosting; Computer Appl. in Social and Behavioral Sciences; Econometrics; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Forecasting; Macroeconomic time series; Macroeconomics; Math Applications in Computer Science; Nonlinear; Operations Research/Decision Theory; Parameter robustness; Performance evaluation; Robustness; Subgroups; Trees; Variables
• ISSN: 1572-9974; 0927-7099; 1572-9974
• DOI: 10.1007/s10614-023-10421-3

Abstract This paper aims to elaborate a treated data set and apply the boosting methodology to monthly Brazilian macroeconomic variables to check its predictability. The forecasting performed here consists in using linear and nonlinear base-learners, as well as a third type of model that has both linear and nonlinear components in the estimation of the variables using the history itself with lag up to 12 periods. We want to investigate which models and for which forecast horizons we have the strongest performance. The results obtained here through different evaluation approaches point out that, on average, the performance of boosting models using P-Splines as base-learner are the ones that have the best results, especially the methodology with two components: two-stage boosting. In addition, we conducted an analysis on a subgroup of variables with data available until 2022 to verify the validity of our conclusions. We also compared the performance of boosted trees with other models and evaluated model parameters using both cross-validation and Akaike Information Criteria in order to check the robustness of the results.