Методы машинного обучения в макроэкономическом прогнозировании: предварительные итоги

Вопросы экономики. 2025. № 10. С. 131–154.

Smirnov S. V.

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. 

Keywords: макроэкономика, машинное обучение, forecasting, прогнозирование, macroeconomics, nowcasting, наукастинг, machine learning

Publication based on the results of: Development of macro forecasting tools that are effective in conditions of sudden changes in trends and structural changes in the economy, using expert surveys and big data (2024)