What type of study is this?

This is a Quantitative Study study.

October 13, 2025

Machine learning methods in macroeconomic forecasting: Preliminary results

Key Points

Machine learning methods have shown potential in improving forecasting accuracy compared to traditional techniques.
Statistical significance of improvements varies, with many being too small to impact practical implementation.
Assessments of machine learning's performance in forecasting macro indicators highlight its usefulness with traditional data.
The emergence of big and unstructured data makes machine learning increasingly essential for macroeconomic analysis.

Abstract

The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and standard econometric benchmarks, but improvements are not always statistically significant and, when they are, may be too small to matter for practitioners once implementation costs are considered. We highlight several tasks where ML is already useful even with traditional data and stress that ML becomes indispensable with “big” and unstructured data.

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