Regional inflation spillovers in the Russian Federation

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Abstract

Introduction. A large number of studies have been devoted to the study of spillover effects in many sectors of the economy. However, inflation spillovers in regional data from Russia are studied for the first time. Theoretical analysis. This paper aims at studying inflation spillovers in the aggregate and 12 subgroups of the CPI in 26 regions of the Russian Federation. To achieve this goal, we use the Diebold and Yilmaz methodology to quantitatively assess the spillover effect for the consumer price index. Diebold and Yilmaz proposed to calculate a number of indices based on the decomposition of the variance vector autoregressive model to characterize the connectivity of the system at various levels, from pairwise to system-wide. The decomposition of the variance shows what part of the future uncertainty of region i is associated with shocks in region j. The article implements three vector autoregressive models (VAR) with thinning of coefficients (with a penalty) – the Elastic Net, Lasso and Ridge VAR models. Empirical analysis. The dynamic relationship of random shocks (spillover effects) of inflation between regions of the Russian Federation and the stability of the selected methods are studied. To calculate the spillover measures, a technique based on the decomposition of the forecast variance of multivariate time series is used. Clustering methods are used to identify spatial macroregions of the spread of inflation spillovers. Results. The transmission of random shocks on a regional basis during the period from January 2002 to June 2024 occurs from the central regions to the Siberian regions and to the Far Eastern regions. The results of the study provide new information on the heterogeneity of inflation spillovers between Russian regions.

About the authors

Yulia Igorevna Krotova

Saratov State University

ORCID iD: 0000-0002-9434-9806
410028, Russia, Saratov, Astrakhanskaya str., 83

V. A. Balash

Saratov State University

ORCID iD: 0000-0002-6987-4799
410028, Russia, Saratov, Astrakhanskaya str., 83

Alexey Raisovich Faizliev

Saratov State University

ORCID iD: 0000-0001-6442-4361
410028, Russia, Saratov, Astrakhanskaya str., 83

References

  1. Файзлиев А. Р., Балаш В. А. Устойчивость методов оценки переливов волатильности // Математическое и компьютерное моделирование в экономике, страховании и управлении рисками : сб. материалов конференций. 2023. Вып. 8. С. 174–178. EDN: GNCHOC
  2. Cecchetti S. G., Mark N. C., Sonora R. J. Price index convergence among United States cities // International Economic Review. 2002. Vol. 43, iss. 4. P. 1081–1099. https://doi.org/10.1111/1468-2354.t01-1-00049
  3. Huang H.-Ch., Lin P.-Ch., Yeh Ch.-Ch. Price level convergence across cities? Evidence from panel unit root tests // Applied Economics Letters. 2010. Vol. 18, iss. 1. P. 87–93. https://doi.org/10.1080/13504850903425157
  4. Kitenge E. M., Morshed A. K. M. Price convergence among Indian cities: The role of linguistic differences, topography, and aggreg ation // Journal of Asian Economics. 2019. Vol. 61. P. 34–50. https://doi.org/10.1016/j.asieco.2019.02.002
  5. Yazgan M. E., Yilmazkuday H. Price-level convergence: New evidence from U.S. cities // Economics Letters. 2011. Vol. 110. iss. 2. P. 76–78. https://doi.org/10.1016/j.econlet.2010.10.013
  6. Балаш О. С. Пространственный анализ конвергенции регионов России // Известия Саратовского университета. Новая серия. Серия: Экономика. Управление. Право. 2012. Т. 12, вып. 4. P. 45–52. https://doi.org/10.18500/1994-2540-2012-12-4-45-52
  7. Мамонтов Д. С., Островская Е. А. Региональная конвергенция: подход на основе географически взвешенной регрессии // Доклады об экономических исследованиях. 2022. № 98. 37 с. URL: https://cbr.ru/StaticHtml/File/138725/wp_98.pdf (дата обращения: 08.09.2024).
  8. Diebold F. X., Yilmaz K. Measuring fi nancial asset return and volatility spillovers, with application to global equity markets // The Economic Journal. 2009. Vol. 119, iss. 534. P. 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
  9. Diebold F. X., Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers // International Journal of Forecasting. 2012. Vol. 28, iss. 1. P. 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
  10. Chow W. W., Fung M. K., Cheng A. C. S. Convergence and spillover of house prices in Chinese cities // Applied Economics. 2016. Vol. 48, iss. 51. P. 4922–4941. https://doi.org/10.1080/00036846.2016.1167829
  11. Cronin D. The interaction between money and asset markets: A spillover index approach // Journal of Macroeconomics. 2014. Vol. 39, pt. A. P. 185–202. https://doi.org/10.1016/j.jmacro.2013.09.006
  12. Diebold F. X., Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms // Journal of Econometrics. 2014. Vol. 182, iss. 1. P. 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
  13. Yang X., Zhang, Y., Li Q. The role of price spillovers: What is different in China // Empirical Economics. 2021. Vol. 60, iss. 1. P. 459–485. https://doi.org/10.1007/s00181-020-01989-y
  14. Balcilar M., Bekun F. V. Spillover dynamics across price infl ation and selected agricultural commodity prices // Journal of Economic Structures. 2020. Vol. 9. Art. 2. P. 1–17. https://doi.org/10.1186/S40008-020-0180-0
  15. Tiwari A. K., Shahbaz M., Hasim H. M., Elheddad M. M. Analysing the spillover of infl ation in selected Euroarea countries // Journal of Quantitative Economics. 2019. Vol. 17, iss. 3. P. 551–577. https://doi.org/10.1007/s40953-018-0152-5
  16. Çakır M. Regional infl ation spillovers in Turkey // Economic Change and Restructuring. 2023. Vol. 56, iss. 2. P. 959–980. https://doi.org/10.1007/s10644-022-09455-8
  17. Koop G., Pesaran M. H., Potter S. M. Impulse response analysis in nonlinear multivariate models // Journal of Econometrics. 1996. Vol. 74, iss. 1. P. 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
  18. Pesaran H. H., Shin Y. Generalized impulse response analysis in linear multivariate models // Economics Letters. 1998. Vol. 58, iss. 1. P. 17–29. https://doi.org/10.1016/s0165-1765(97)00214-0
  19. Zou H., Hastie T. Regularization and variable selection via the elastic net // Journal of the Royal Statistical Society. Series B: Statistical Methodology. 2005. Vol. 67, iss. 2. P. 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
  20. Tibshirani R. Regression shrinkage and selection via the lasso: A retrospective // Journal of the Royal Statistical Society. Series B: Statistical Methodology. 2011. Vol. 73, iss. 3. P. 273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x

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