Scientific Journal SPATIAL ECONOMICS - Spatial Modelling of the Food Price Dynamics

In the section	Articles
Title of the article	Spatial Modelling of the Food Price Dynamics
Pages	67-92
Author 1	Evgeny Sergeevich Inozemtsev Candidate of Sciences (Economics), Сhief Еconomist Saratov Regional Division of the Volgo-Vyatka Main Branch 2 Sovietskaya St., Saratov, 410028, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0002-0146-3395
Author 2	Vitaly Georgiyevich Bogoyavlenskiy Leading Еconomist Saratov Regional Division of the Volgo-Vyatka Main Branch 2 Sovietskaya St., Saratov, 410028, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0001-8625-1449
Abstract	The paper explores the impact of business indicators of the Bank of Russia’s monitoring on the monthly food prices growth rates, considering spatial effects. As a baseline, we used an adaptive Phillips curve with monitoring indicators as a gauge of economic activity (business climate indicator of the food industry or price expectations of the industry). Their advantages over traditional ones (e.g., the output gap): timeliness, high frequency, consideration of latent factors, independence on methodology, absence of revision risks. Calculations using three special cases of the spatiotemporal autoregressive Durbin model show a significant impact of food industry’s price expectations and their spatial lag on the food consumer price index (CPI) next month. A similar effect is confirmed for the spatial lag of CPI inertia. The model with both spatial lags of CPI (W?t) and CPI inertia (W?t – 1) didn’t yield satisfactory results. We proposed an asymmetric weights matrix that takes into account along with distance also direction of spatial interregional influence, allowing for more robust estimates. This indirectly confirms the hypothesis that the main factor of spatial correlation of food CPI is interregional transportation whose impact may manifest itself with a time lag. The study substantiates the potential of spatial lags for other models using the business monitoring data. The obtained results can be used to refine short-term forecast of regional CPI and, ultimately, to improve the quality of data for Bank of Russia’s monetary policy
Code	332.1+330.4
JEL	C21, E31, R10
DOI	https://dx.doi.org/10.14530/se.2026.1.067-092
Keywords	Bank of Russia, regional inflation, monitoring of enterprises, spatial effects, Darbin spatial model, method of moments
Download	SE.2026.1.067-092.Inozemtsev.pdf
For citation	Inozemtsev E.S., Bogoyavlenskiy V.G. Spatial Modelling of the Food Price Dynamics. Prostranstvennaya Ekonomika = Spatial Economics, 2026, vol. 22, no. 1, pp. 67–92. https://dx.doi.org/10.14530/se.2026.1.067-092 (In Russian)
References	1. Aginta H. Spatial Dynamics of Consumer Price in Indonesia: Convergence Clubs and Conditioning Factors. Asia-Pacific Journal of Regional Science, 2020, vol. 5, pp. 427–451. https://doi.org/10.1007/s41685-020-00178-0 2. Aginta H. Spatiotemporal Analysis of Regional Inflation in an Emerging Country: The Case of Indonesia. Regional Science Policy & Practice, 2022, vol. 14, issue 3, pp. 667–688. https://doi.org/10.1111/rsp3.12539 3. Beck N., Katz J. Time-Series – Cross-Section Issues: Dynamic. New York University, 2004, 35 p. Available at: https://www.researchgate.net/publication/228723029_Time-Series-Cross-Section_Issues_Dynamics_2004 (accessed January 2026). 4. Cook S.J., Hays J.C., Franzese Jr.R.J. STADL Up! The Spatio-Temporal Autoregressive Distributed Lag Model for TSCS Data Analysis. American Political Science Review, 2022, vol. 117, issue 1, pp. 362–364. https://doi.org/10.1017/s0003055422001101 5. Demidova O.А. Methods of Spatial Econometrics and Evaluation of Government Programs Effectiveness. Prikladnaya Ekonometrika = Applied Econometrics, 2021, no. 4 (64), pp. 107–134. (In Russian).https://doi.org/10.22394/1993-7601-2021-64-107-134 6. Gimpelson V. The Labor Market in Russia, 2000–2017. IZAWorld of Labor, 2019. https://doi.org/10.15185/izawol.466 7. Inozemtsev E.S., Krotova Yu.I. Phillips Curve With Spatial Effects Based on Russian Regional Data. Zhurnal Novoy Ekonomicheskoy Assotsiatsii = Journal of the New Economic Association, 2024, no. 2 (63), pp. 35–56. https://doi.org/10.31737/22212264_2024_2_35-56 (In Russian). 8. Kartaev PH.S., Besedovskaya M.N. Is The Phillips Curve Useful for Forecasting Inflation in Russia? Vestnik Moskovskogo Universiteta. Seriya 6. Ekonomika = Moscow University Economics Bulletin, 2023, vol. 58, no. 6, pp. 24–43. https://doi.org/10.55959/msu0130-0105-6-58-6-2 (In Russian). 9. Kirillov A.М. A Study on Spatial Autocorrelation: Case of Russian Regional Inflation. Prikladnaya Ekonometrika = Applied Econometrics, 2021, no. 4 (64), pp. 5–22. https://doi.org/10.22394/1993-7601-2021-64-5-22 (In Russian). 10. Kirillov A.М. Spatial Analysis of Food Inflation in Russian Regions. Prostranstvennaya Ekonomika = Spatial Economics, 2017, no. 4, pp. 41–58. https://doi.org/10.14530/se.2017.4.041-058 (In Russian). 11. Kobzev A.Yu., Andreev A.V. Business Activity and Inflation Indicators Based on Monitoring of Business. Bank of Russia, 2021, 20 p. Available at: https://cbr.ru/content/document/file/119543/analytic_note_20210322.pdf (accessed January 2026). (In Russian). 12. Krotova Yu.I., Balash V.A., Faizliev A.R. Regional Inflation Spillovers in the Russian Federation. Izvestiya Saratovskogo Universiteta. Novaya Seriya. Seriya: Ekonomika. Upravlenie. Pravo = Izvestiya of Saratov University. Economics. Management. Law, 2025, vol. 25, no. 1, pp. 27–37. https://doi.org/10.18500/1994-2540-2025-25-1-27-37(In Russian). 13. Krylov D.V. Food Inflation in Russia and World Food Prices. Bank of Russia, 2024, 49 p. Available at: https://cbr.ru/statichtml/file/158568/wp_126.pdf (accessed January 2026). (In Russian). 14. Lee L., Yu J. Identification of Spatial Durbin Panel Models. Journal of Applied Economet-rics, 2015, vol. 31, issue 1, pp. 133–162. https://doi.org/10.1002/jae.2450 15. LeSage J.P., Pace R.K. Introduction to Spatial Econometrics. CRC Press, 2009, 340 р. 16. Lyakhnova M., Kolenko Yu. Nowcasting the Output Gap in Russia using Enterprise Monitoring Data. Denigi i Kredit = Russian Journal of Money and Finance, 2024, vol. 83, no. 2, pp. 26–53. (In Russian). 17. Marques H., Pino G., Tena J.D. Regional Inflation Dynamics Using Space-Time Models. SSRN Electronic Journal, 2009, 26 р. https://doi.org/10.2139/ssrn.1515732 18. Mutl J., Pfafermayr M. The Hausman Test in a Cliff and Ord Panel Model. Econometrics Journal, 2011, vol. 14, issue 1, pp. 48–76. https://doi.org/10.1111/j.1368-423X.2010.00325.x 19. Nagayasu J. Regional Inflation, Spatial Locations and the Balassa-Samuelson Effect: Evidence from Japan. Urban Studies, 2017, vol. 54, issue 6, pp. 1482–1499. https://doi.org/10.1177/0042098016629915 20. Perevyshin Yu.N., Sinelnikov-Murylev S.G., Trunin P.V. Determinants of Price Differentiation across Russian Regions. Ekonomicheskiy Zhurnal Vysshey Shkoly Ekonomiki = Higher School of Economics Economic Journal, 2017, vol. 21, no. 3, pp. 361–384. (In Russian). 21. Perevyshin Yu.N., Skrobotov A.A. The Price Convergence of Individual Goods in the Russian Regions. Zhurnal Novoy Ekonomicheskoy Assotsiatsii = Journal of the New Economic Association, 2017, no. 3 (35), pp. 102–123. https://doi.org/10.31737/2221-2264-2017-35-3-4 (In Russian). 22. Petrova D.A. Inflation Forecasting Based on Internet Search Queries. Ekonomicheskoe Razvitie Rossii = Russian Economic Developments, 2019, vol. 26, no. 11, pp. 55–62. (In Russian). 23. Semiturkin O.N., Shevelev A.A. Kvaktun M.I. Analysis of the Heterogeneity Factors and Assessment of the Structural Levels of Inflation in Russian Regions. Voprosy Ekonomiki [Economic Issues], 2021, no. 9, pp. 51–68. https://doi.org/10.32609/0042-8736-2021-9-51-68 (In Russian). 24. Shin Y., Thornton M. The Spatio-Temporal Autoregressive Distributed Lag Modelling Approach to an Analysis of Dynamic Networks. University of York, 2019, 51 p. Available at: https://fec2019.sciencesconf.org/data/program/_yc_STARDL_F4_2_1.pdf (accessed February 2026). 25. Tishin A.V., Khabibullin R.A. Using Unobserved Variables in Inflation Risk Analysis. Bank of Russia, 2020, 14 p. Available at: https://cbr.ru/Content/Document/File/108481/analytic_note_20200630_dip.pdf (accessed January 2026). (In Russian). 26. Volgina E. Forecasting Inflation Using News Indices. Denigi i Kredit = Russian Journal of Money and Finance, 2025, vol. 84, no. 1, pp. 26–59. (In Russian). 27. Yanulevich M.V. On the Predictive Power of the Price Expectations Indicator Based on Monitoring of Business. Bank of Russia, 2024. 40 c. Available at: https://cbr.ru/Content/Document/File/167898/analytic_note_20241107_irkutsk.pdf (accessed January январь 2026). (In Russian). 28. Yesilyurt F., Elhorst J.P. A Regional Analysis of Inflation Dynamics in Turkey. Annals of Regional Science, 2014, vol. 52, pp. 1–17. https://doi.org/10.1007/s00168-013-0570-4 29. Zhang H., Wang X. Combined Asymmetric Spatial Weights Matrix with Application to Housing Prices. Journal of Applied Statistics, 2017, vol. 44, issue 13, pp. 2337–2353. https://doi.org/10.1080/02664763.2016.1254163 30. Zhemkov M.I. Regional Effects of Inflation Targeting in Russia: Factors of Heterogeneity and Structural Inflation Rates. Voprosy Ekonomiki [Economic Issues], 2019, no. 9, pp. 70–89. (In Russian).https://doi.org/10.32609/0042-8736-2019-9-70-89 31. Zubarev A.V., Trunin P.V. The Analysis of the Dynamics of the Russian Economy Using the Output Gap Indicator. Problemy Prognozirovaniya = Studies on Russian Economic Development, 2017, no. 2 (161), pp. 10–17. (In Russian).
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Submitted	13.02.2026
Approved after reviewing	12.03.2026
Accepted for publication	16.03.2026
Available online	10.04.2026