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Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?
Author(s) -
Zeng Jing
Publication year - 2017
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2415
Subject(s) - boosting (machine learning) , aggregate (composite) , econometrics , gradient boosting , computer science , feature selection , variable (mathematics) , variables , machine learning , economics , mathematics , random forest , mathematical analysis , materials science , composite material
Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an economic aggregate may improve forecasting accuracy. In this paper we suggest using the boosting method to select the disaggregate variables, which are most helpful in predicting an aggregate of interest. We conduct a simulation study to investigate the variable selection ability of this method. To assess the forecasting performance a recursive pseudo‐out‐of‐sample forecasting experiment for six key euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a feasible and competitive approach in forecasting an aggregate. Copyright © 2016 John Wiley & Sons, Ltd.