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A study on spatial autocorrelation: Case of Russian regional inflation

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  • Andrew Kirillov

    (AO «ALFA-BANK»; Moscow, Russian Federation)

Abstract

We apply APLE statistic to explore spatial autocorrelation of Russian regional inflationary processes. APLE is discussed to be the fine alternative to Moran’s I. To conduct this study we modify statistics of spatial dependence for panel data structure. We use time series of Russian regional CPIs (i.e. quantitative measure of inflation) of food, non-food, services baskets. We find evidence to confirm the hypothesis of the existence of spatial autocorrelation of regional inflationary processes on the horizon of our study.

Suggested Citation

  • Andrew Kirillov, 2021. "A study on spatial autocorrelation: Case of Russian regional inflation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 5-22.
    • Handle: RePEc:ris:apltrx:0430
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    References listed on IDEAS

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    1. Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
    2. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    3. Konstantin Gluschenko, 2010. "The Law of One Price in the Russian Economic Space," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 3-19.
    4. A. Kudrin., 2009. "Global Financial Crisis and Its Impact on Russia," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 1.
    5. Konstantin Gluschenko, 2016. "Distribution dynamics of Russian regional prices," Empirical Economics, Springer, vol. 51(3), pages 1193-1213, November.
    6. K. Yudaeva., 2014. "On the Opportunities, Targets and Mechanisms of Monetary Policy under the Current Conditions," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 9.
    7. Venera Maratovna Timiryanova, 2017. "Spatial Relation of the Consumer Price Index and the Equilibrium of Regional Goods Markets," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 2, pages 95-109.
    8. Li, Hongfei & Calder, Catherine A. & Cressie, Noel, 2012. "One-step estimation of spatial dependence parameters: Properties and extensions of the APLE statistic," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 68-84.
    9. A. Kudrin., 2007. "Inflation: Recent Trends in Russia and in the World," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 10.
    10. Konstantin Pavlovich Gluschenko, 2007. "Price Linkages of Russian Regional Markets," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 1, pages 48-60.
    11. Andrey Mikhailovich Kirillov, 2017. "Spatial Analysis of Food Inflation in Russian Regions," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 4, pages 41-58.
    12. Fackler, Paul L. & Goodwin, Barry K., 2001. "Spatial price analysis," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 17, pages 971-1024, Elsevier.
    13. Vera Ivanovna Ivanova, 2014. "Regional Convergence of Income: Spatial Analysis," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 4, pages 100-119.
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    Cited by:

    1. Inozemtsev, E. & Krotova, Yu., 2024. "Phillips curve with spatial effects based on Russian regional data," Journal of the New Economic Association, New Economic Association, vol. 63(2), pages 35-56.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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