Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Medeiros, Marcelo C.; Vasconcelos, Gabriel F. R.; Veiga, Álvaro; Zilberman, Eduardo
• Is Part Of: Journal of business & economic statistics. Volume 39:Issue 1(2021)
• Page Start: 98
• Page End: 119
• Publication Date: 2021-01-02
• Subjects: Big data -- Inflation forecasting -- LASSO -- Machine learning -- Random forests; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2019.1637745
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract: Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast U.S. inflation. Despite the skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation. Supplementary materials for this article are available online.