IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0442.html
   My bibliography  Save this article

Modeling COVID-19 spread in the Russian Federation using global VAR approach

Author

Listed:
  • Zubarev, Andrei

    (RANEPA; Moscow, Russian Federation)

  • Kirillova, Maria

    (RANEPA; Moscow, Russian Federation)

Abstract

The aim of this study is to analyse the spread of COVID-19 in Russia taking into account various connections between regions and the effectiveness of isolation strategies using global vector autoregression (GVAR). We use regional data on new cases of coronavirus, self-isolation index, Google trends index reflecting social awareness of pandemic and passenger turnover in buses, trains and planes. It was found that the number of new COVID-19 cases reacts to Moscow outbreak shock significantly in most regions. During the second wave, the speed of reaction was faster but of a smaller size. The forecasts of new cases dynamics during the rising of the second wave turn out to be rather close to the actual dynamics in many regions. More rigorous social distancing strategy in Moscow reduced the number of cases in some regions but at the same time raised that number in some others

Suggested Citation

  • Zubarev, Andrei & Kirillova, Maria, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.
    • Handle: RePEc:ris:apltrx:0442
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2022_65_117-138.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ludovic Gauvin & Cyril C. Rebillard, 2018. "Towards recoupling? Assessing the global impact of a Chinese hard landing through trade and commodity price channels," The World Economy, Wiley Blackwell, vol. 41(12), pages 3379-3415, December.
    2. Bettendorf, Timo, 2017. "Investigating Global Imbalances: Empirical evidence from a GVAR approach," Economic Modelling, Elsevier, vol. 64(C), pages 201-210.
    3. S. P. Zemtsov & V. L. Baburin, 2020. "COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions," Regional Research of Russia, Springer, vol. 10(3), pages 273-290, July.
    4. Kamiar Mohaddes & Mehdi Raissi, 2019. "The US oil supply revolution and the global economy," Empirical Economics, Springer, vol. 57(5), pages 1515-1546, November.
    5. Stephane Dees & M. Hashem Pesaran & L. Vanessa Smith & Ron P. Smith, 2009. "Identification of New Keynesian Phillips Curves from a Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1481-1502, October.
    6. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    7. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    8. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
    9. Chudik, Alexander & Mohaddes, Kamiar & Pesaran, M. Hashem & Raissi, Mehdi & Rebucci, Alessandro, 2021. "A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model," Journal of International Money and Finance, Elsevier, vol. 119(C).
    10. Cesa-Bianchi, Ambrogio, 2013. "Housing cycles and macroeconomic fluctuations: A global perspective," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 215-238.
    11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    12. Mohaddes, Kamiar & Pesaran, M. Hashem, 2016. "Country-specific oil supply shocks and the global economy: A counterfactual analysis," Energy Economics, Elsevier, vol. 59(C), pages 382-399.
    13. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    14. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2023. "How to go viral: A COVID-19 model with endogenously time-varying parameters," Journal of Econometrics, Elsevier, vol. 232(1), pages 70-86.
    15. Cashin, Paul & Mohaddes, Kamiar & Raissi, Maziar & Raissi, Mehdi, 2014. "The differential effects of oil demand and supply shocks on the global economy," Energy Economics, Elsevier, vol. 44(C), pages 113-134.
    16. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    17. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    18. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zubarev, Andrey & Kirillova, Maria, 2021. "Эконометрическая Оценка Влияния Шоков На Рынке Нефти На Макроэкономические Показатели Российской Федерации С Помощью Gvar Моделирования [The Impact of Oil Market Shocks on the Macroeconomic Indicat," MPRA Paper 110410, University Library of Munich, Germany, revised 01 Nov 2021.
    2. Zubarev, Andrey & Kirillova, Maria, 2022. "Оценивание Влияния Внешних Шоков На Российскую Экономику С Помощью Модели Gvar [Estimating the impact of external shocks on Russian economy: GVAR approach]," MPRA Paper 113762, University Library of Munich, Germany, revised 01 Jul 2022.
    3. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    4. Cashin, Paul & Mohaddes, Kamiar & Raissi, Mehdi, 2017. "China's slowdown and global financial market volatility: Is world growth losing out?," Emerging Markets Review, Elsevier, vol. 31(C), pages 164-175.
    5. Kamiar Mohaddes & Mehdi Raissi, 2019. "The US oil supply revolution and the global economy," Empirical Economics, Springer, vol. 57(5), pages 1515-1546, November.
    6. Chudik, Alexander & Mohaddes, Kamiar & Pesaran, M. Hashem & Raissi, Mehdi & Rebucci, Alessandro, 2021. "A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model," Journal of International Money and Finance, Elsevier, vol. 119(C).
    7. Considine, Jennifer & Hatipoglu, Emre & Aldayel, Abdullah, 2022. "The sensitivity of oil price shocks to preexisting market conditions: A GVAR analysis," Journal of Commodity Markets, Elsevier, vol. 27(C).
    8. Burney, Nadeem A. & Mohaddes, Kamiar & Alawadhi, Ahmad & Al-Musallam, Marwa, 2018. "The dynamics and determinants of Kuwait's long-run economic growth," Economic Modelling, Elsevier, vol. 71(C), pages 289-304.
    9. Wei, Honghong & Lahiri, Radhika, 2019. "The impact of commodity price shocks in the presence of a trading relationship: A GVAR analysis of the NAFTA," Energy Economics, Elsevier, vol. 80(C), pages 553-569.
    10. D. O. Olayungbo & Chisom Umechukwu, 2022. "Asymmetric oil price shocks and the economies of selected oil-exporting African countries: a global VAR approach," Economic Change and Restructuring, Springer, vol. 55(4), pages 2137-2170, November.
    11. Dovern, Jonas & van Roye, Björn, 2014. "International transmission and business-cycle effects of financial stress," Journal of Financial Stability, Elsevier, vol. 13(C), pages 1-17.
    12. Mala Raghavan & Faisal Khan & Sonia Kumari Selvarajan & Evelyn S. Devadason, 2023. "Cross‐country linkages between ASEAN and non‐ASEAN‐RCEP member states: A global VAR analysis," The World Economy, Wiley Blackwell, vol. 46(6), pages 1782-1814, June.
    13. Konstantinos N. Konstantakis & Panayotis G. Michaelides & Livia Chatzieleftheriou & Arsenios‐Georgios N. Prelorentzos, 2022. "Crisis and the Chinese miracle: A network—GVAR model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 900-921, July.
    14. Dees, Stéphane, 2016. "Credit, asset prices and business cycles at the global level," Economic Modelling, Elsevier, vol. 54(C), pages 139-152.
    15. Smith, L. Vanessa & Tarui, Nori & Yamagata, Takashi, 2021. "Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions," Energy Economics, Elsevier, vol. 97(C).
    16. Osorio, Carolina & Unsal, D. Filiz, 2013. "Inflation dynamics in Asia: Causes, changes, and spillovers from China," Journal of Asian Economics, Elsevier, vol. 24(C), pages 26-40.
    17. Mariam Camarero & Josep Lluís Carrion‐i‐Silvestre & Cecilio Tamarit, 2021. "External imbalances from a GVAR perspective," The World Economy, Wiley Blackwell, vol. 44(11), pages 3202-3245, November.
    18. Dovern, Jonas & van Roye, Björn, 2013. "International transmission of financial stress: Evidence from a GVAR," Kiel Working Papers 1844, Kiel Institute for the World Economy (IfW Kiel).
    19. Mohaddes, Kamiar & Pesaran, M. Hashem, 2017. "Oil prices and the global economy: Is it different this time around?," Energy Economics, Elsevier, vol. 65(C), pages 315-325.
    20. Anna Sznajderska, 2019. "The role of China in the world economy: evidence from a global VAR model," Applied Economics, Taylor & Francis Journals, vol. 51(15), pages 1574-1587, March.

    More about this item

    Keywords

    COVID-19; pandemic; global VAR; infection; cross-country spillovers; Google trends; second wave forecast;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.