voprecotestВопросы экономикиVoprosy Ekonomiki0042-8736Voprosy Ekonomiki, NP10.32609/0042-8736-2025-10-131-154voprecotest-5470Research ArticleМЕТОДОЛОГИЯ ЭКОНОМИЧЕСКОГО АНАЛИЗАMETHODOLOGY OF ECONOMIC ANALYSISМетоды машинного обучения в макроэкономическом прогнозировании: предварительные итогиMachine learning methods in macroeconomic forecasting: Preliminary resultshttps://orcid.org/0000-0001-5177-2578СмирновС. В.SmirnovS. V.

Смирнов Сергей Владиславович, к. э. н., замдиректора Института «Центр развития»

Москва

Sergey V. Smirnov

Moscow

 

svsmirnov@hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияHSE UniversityRussian Federation202510102025010131154Copyright © Voprosy Ekonomiki, NP, 20252025Voprosy Ekonomiki, NPVoprosy Ekonomiki, NPhttps://www.vopreco.ru/jour/about/submissions#copyrightNoticehttps://www.vopreco.ru/jour/article/view/5470

Систематизированы методы машинного обучения (ММО), наиболее релевантные для макроэкономики, суммированы результаты их применения для прогнозирования и наукастинга ключевых макроэкономических показателей. Показано, что, несмотря на методологический прогресс и публикации последних лет, точность на традиционных статистических данных не растет: ММО нередко превосходят наивные и стандартные бенчмарки, однако прирост точности не всегда статистически значим и заметен для практиков с учетом издержек внедрения. Отмечены три прикладные задачи, где ММО уже полезны даже на традиционных данных; при этом основной потенциал ММО раскрывается при работе с «большими» и неструктурированными данными, где они фактически незаменимы. 

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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The authors declare that there are no conflicts of interest present.