Прогнозирование региональной инфляции: эконометрические модели или методы машинного обучения?

Экономический журнал Высшей школы экономики. 2024. Т. 28. № 1. С. 81–107.

Bukina T. V., Kashin D.

The paper reveals the forecasts for regional inflation based on the regions of the Privolzhskiy Federal District (PFD). The purpose of the study is to determine the model that most accurately predicts regional inflation. The paper compares the tools of machine learning – support vector machines, gradient boosting, and random forest – with econometric models of time series, autoregression and integrated autoregression-moving average – models that are often used to predict national inflation in Russia. The results of the study help to identify the key macroeconomic indicators that most significantly affect regional inflation. The top three of them for each region include the value of inflation within last month, the average inflation, and the count number of analyzed month. We show that machine learning models are not as bad at predicting regional inflation over long time periods (21 months and 24 months), while econometric models predict quite accurately on short time horizons.

Keywords: эконометрическое моделирование, машинное обучение, forecasting, прогнозирование, econometric modeling, machine learning, regional inflation, Региональная инфляция, случайный лес, Gradient boosting, градиентный бустинг, random forest