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Forecasting with many predictors using Bayesian additive regression trees
Author(s) -
Prüser Jan
Publication year - 2019
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.2587
Subject(s) - econometrics , regression , exploit , computer science , context (archaeology) , bayesian vector autoregression , bayesian probability , econometric model , time series , key (lock) , regression analysis , economics , machine learning , artificial intelligence , statistics , paleontology , mathematics , computer security , biology
Forecasting with many predictors provides the opportunity to exploit a much richer base of information. However, macroeconomic time series are typically rather short, raising problems for conventional econometric models. This paper explores the use of Bayesian additive regression trees (Bart) from the machine learning literature to forecast macroeconomic time series in a predictor‐rich environment. The interest lies in forecasting nine key macroeconomic variables of interest for government budget planning, central bank policy making and business decisions. It turns out that Bart is a valuable addition to existing methods for handling high dimensional data sets in a macroeconomic context.