Abstract
Abstract
Research objective. This paper compares the power of structural and technical approaches to inflation forecasting, identifying the model with the lowest forecast error and the most stable results. Materials and methods . The study uses Russian data from 2010 to 2024, covering several business cycles and structural shifts. The selected factors are variables justified by economic theory. The study provides an overview of the following methods: naive forecasting, ARIMA, VAR models, random forest, gradient boosting, etc. Conclusion . Technical models generally demonstrated higher forecasting accuracy compared to structural models. Machine learning methods are promising in forecasting of inflationary processes in Russia.
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@article{2026Inflation,
title = {Inflation Forecasting in Russia: Structural and Technical Approaches},
author = {А.А. Мухамадиева and Elena Sinelnikova-Muryleva},
journal = {The world of new economy},
year = {2026},
doi = {10.26794/2220-6469-2026-20-3-102-115},
url = {https://doi.org/10.26794/2220-6469-2026-20-3-102-115}
}
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