IDEAS home Printed from https://ideas.repec.org/a/eee/inteco/v174y2023icp69-90.html
   My bibliography  Save this article

The economic impact of conflict-related and policy uncertainty shocks: The case of Russia

Author

Listed:
  • Diakonova, Marina
  • Ghirelli, Corinna
  • Molina, Luis
  • Pérez, Javier J.

Abstract

We show that policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of “conflict”, referring to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence, and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model: we show that the indicators provide useful information for forecasting GDP in the short run, even when controlling for a comprehensive set of standard high-frequency macro-financial variables. The second approach is a SVAR model. We show that negative shocks to the selected indicators yield economic slowdown, with a persistent drop in GDP growth and a short-lived but large increase in country risk.

Suggested Citation

  • Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
  • Handle: RePEc:eee:inteco:v:174:y:2023:i:c:p:69-90
    DOI: 10.1016/j.inteco.2023.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2110701723000215
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.inteco.2023.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    2. Asaf Zussman & Noam Zussman, 2006. "Assassinations: Evaluating the Effectiveness of an Israeli Counterterrorism Policy Using Stock Market Data," Journal of Economic Perspectives, American Economic Association, vol. 20(2), pages 193-206, Spring.
    3. Timothy Besley & Hannes Mueller, 2018. "Predation, Protection, and Productivity: A Firm-Level Perspective," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(2), pages 184-221, April.
    4. Simon Johnson & John McMillan & Christopher Woodruff, 2002. "Property Rights and Finance," American Economic Review, American Economic Association, vol. 92(5), pages 1335-1356, December.
    5. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    6. Hannes Mueller & Christopher Rauh, 2022. "Using past violence and current news to predict changes in violence," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 579-596, July.
    7. Timothy Besley & Hannes Mueller, 2012. "Estimating the Peace Dividend: The Impact of Violence on House Prices in Northern Ireland," American Economic Review, American Economic Association, vol. 102(2), pages 810-833, April.
    8. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Mr. Philip Barrett & Maximiliano Appendino & Kate Nguyen & Jorge de Leon Miranda, 2020. "Measuring Social Unrest Using Media Reports," IMF Working Papers 2020/129, International Monetary Fund.
    11. Charemza, Wojciech & Makarova, Svetlana & Rybiński, Krzysztof, 2022. "Economic uncertainty and natural language processing; The case of Russia," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 546-562.
    12. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    13. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    14. repec:hal:spmain:info:hdl:2441/4mupcmg7bt8iv8k3lhvbqr8p51 is not listed on IDEAS
    15. Hadzi-Vaskov Metodij & Pienknagura Samuel & Ricci Luca Antonio, 2023. "The Macroeconomic Impact of Social Unrest," The B.E. Journal of Macroeconomics, De Gruyter, vol. 23(2), pages 917-958, June.
    16. Sergei Guriev & Nikita Melnikov, 2016. "War, Inflation, and Social Capital," American Economic Review, American Economic Association, vol. 106(5), pages 230-235, May.
    17. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    18. Boeckelmann Lukas & Stalla-Bourdillon Arthur, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    19. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
    20. Sergei Guriev & Nikita Melnikov, 2016. "War, Inflation, and Social Capital," American Economic Review, American Economic Association, vol. 106(5), pages 230-235, May.
    21. Ghirelli, Corinna & Pérez, Javier J. & Urtasun, Alberto, 2019. "A new economic policy uncertainty index for Spain," Economics Letters, Elsevier, vol. 182(C), pages 64-67.
    22. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    23. Luis J. Álvarez & Florens Odendahl, 2022. "Data outliers and Bayesian VARs in the Euro Area," Working Papers 2239, Banco de España.
    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. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    2. Mr. Philip Barrett & Sophia Chen & Miss Mali Chivakul & Ms. Deniz O Igan, 2021. "Pricing Protest: The Response of Financial Markets to Social Unrest," IMF Working Papers 2021/079, International Monetary Fund.
    3. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    4. Mueller, Hannes & Garcia-Uribe, Sandra & Sanz, Carlos, 2020. "Economic Uncertainty and Divisive Politics: Evidence from the "dos Españas"," CEPR Discussion Papers 15479, C.E.P.R. Discussion Papers.
    5. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    6. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
    7. Dai, Peng-Fei & Xiong, Xiong & Zhang, Jin & Zhou, Wei-Xing, 2022. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Resources Policy, Elsevier, vol. 78(C).
    8. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
    9. Timothy Besley & Thiemo Fetzer & Hannes Mueller, 2023. "How Big Is the Media Multiplier? Evidence from Dyadic News Data," CESifo Working Paper Series 10619, CESifo.
    10. Arbatli Saxegaard, Elif C. & Davis, Steven J. & Ito, Arata & Miake, Naoko, 2022. "Policy uncertainty in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    11. García-Uribe, Sandra & Mueller, Hannes & Sanz, Carlos, 2024. "Economic Uncertainty and Divisive Politics: Evidence from the dos Españas," The Journal of Economic History, Cambridge University Press, vol. 84(1), pages 40-73, March.
    12. Corinna Ghirelli & Javier J. Pérez & Alberto Urtasun, 2020. "Economic policy uncertainty in Latin America: measurement using Spanish newspapers and economic spillovers," Working Papers 2024, Banco de España.
    13. Krzysztof Drachal & Daniel González Cortés, 2022. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data," IJERPH, MDPI, vol. 20(1), pages 1-24, December.
    14. Ghirelli, Corinna & Pérez, Javier J. & Urtasun, Alberto, 2021. "The spillover effects of economic policy uncertainty in Latin America on the Spanish economy," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
    15. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    16. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    17. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
    18. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    19. Nonejad, Nima, 2022. "An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample," Finance Research Letters, Elsevier, vol. 47(PB).
    20. Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.

    More about this item

    Keywords

    Russia; Forecasting; Social unrest; Social conflict; Policy uncertainty; Geopolitical risk;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • N16 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - Latin America; Caribbean

    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:eee:inteco:v:174:y:2023:i:c:p:69-90. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/21107017 .

    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.