Agenda

Titolo completo: "Forecasting Inflation in a Data-Rich Environment: the Benefits of Machine Learning Methods"

Relatore: Marcelo C. Medeiros - Pontificia Universidade Catolica, Rio de Janeiro

Abstract: Inflation forecasting is an important but difficult task. In this paper, we explore the advances in machine learning (ML) methods and the availability of new and rich datasets to forecast US inflation over a long period of out-of-sample observations. 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 for several forecasting horizons both in the 1990s and the 2000s. The ML method that deserves more attention is the random forest, which dominated all other models in several cases. The good performance of the random forest method is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation. The results are robust to inflation measures, different samples, levels of macroeconomic uncertainty, and periods of recession and expansion.