Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods

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 US 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.

Keywords: Big Data, Inflation Forecasting, Shrinkage, Factor Models, LASSO, Random Forests, Machine Learning

JEL Classification: C22, E37