What does machine learning say about the drivers of inflation?

TL;DR

This paper uses machine learning, specifically regression trees, to analyze and predict inflation in 20 advanced countries, highlighting the importance of inflation expectations and demonstrating superior predictive performance over traditional models.

Contribution

It introduces a data-driven machine learning approach with regression trees to predict inflation, providing new insights into the role of expectations and outperforming econometric benchmarks.

Findings

Machine learning models achieve lower RMSE than traditional econometric models.

Inflation expectations significantly influence CPI outcomes.

The importance of expectations has declined over the last decade.

Abstract

This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models. Partial effects of inflation expectations on CPI outcomes are also elicited in the paper. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.