Metel Yu.A., Kunitsyna N.N. Disaggregated Approach to Consumer Price Index Modeling: Regional Aspect

Yuri A.  Metel

Stavropol Territory Branch of the Southern Main Directorate of the Central Bank of the Russian Federation, Stavropol, Russian Federation

Natalia  N.  Kunitsyna

North Caucasus Federal University, Stavropol, Russian Federation

Abstract. The paper solves the problem of accuracy improvement of forecasting the consumer price index (CPI), which is a key indicator of inflationary processes. It is used for monetary policy instruments. In contrast to the traditional approach, in which the forecast is made for the aggregated indicator “All goods and services,” which does not allow taking  into account  the  heterogeneity  of the  dynamics  of  goods and  services  categories,  the authors  implemented disaggregated CPI modeling at the regional level. Forecasting was carried out at three levels: CPI as a whole, by basic components (“Food products,” “Non-food products,” and “Services”) and by 78 product groups. Such an  approach makes it possible to identify hidden interrelations and specific factors affecting price dynamics in various segments of the consumer basket, taking into account regional characteristics. Calculations with monthly breakdowns were carried out on the basis of a wide range of methods: traditional econometric models (SARIMA, Prophet, and Ridge regression) and modern machine learning algorithms (Random Forest and CatBoost). The authors applied the principal component method (PCA) and recursive feature elimination (RFE) to improve the accuracy of the predicted data. Accuracy was assessed on the basis of cross-validation. The statistical significance of differences between models was checked using the Diebold-Mariano test. The results revealed that a disaggregated prediction approach provides higher accuracy compared to aggregated models. Particularly noticeable improvements are observed for commodity categories with high price volatility. The findings confirm that detailing the CPI structure in forecasting allows not only to increase the accuracy of estimates but  also  to  obtain  a more  reliable  analytical  basis  for  making  economic  decisions in  monetary  policy.  Authors’ contribution. Yu.A. Metel – development of forecasting methodology, model implementation, calculation, and sampling methods; N.N. Kunitsyna – literature review, description of results, drawing conclusions, and editing article text.

Key words: consumer price index, inflation, SARIMA, decisive trees, gradient boosting, regularization, Feature Selection, principal component method, disaggregated forecasting, inflation expectations.

Citation. Metel Yu.A., Kunitsyna N.N., 2025. Disaggregated Approach to Consumer Price Index Modeling: Regional Aspect. Regionalnaya ekonomika. Yug Rossii [Regional Economy. South of Russia], vol. 13, no. 4, pp. 118-129. (in Russian). DOI: https://doi.org/10.15688/re.volsu.2025.4.11

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