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

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. for this article are available online.

Automated summary

Inflation forecasting is an important and challenging task, where machine learning methods and new datasets have improved the accuracy of predictions. The random forest model outperforms all other models, demonstrating superior performance due to its specific variable selection method and potential nonlinearities between key macroeconomic variables and inflation.