IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v79y2020i4p98-112.html
   My bibliography  Save this article

A Multi-Country BVAR Model for the External Sector

Author

Listed:
  • Olga Korotkikh

    (Bank of Russia)

Abstract

This paper describes a multi-country BVAR model developed and used by the Monetary Policy Department of the Bank of Russia. The model makes it possible to build coordinated scenario forecasts for the main macro-variables of the USA, the euro area, and China. The simultaneous modelling for the three economies makes it possible to take into account multi-country interactions of the variables and, thus, improve the predictive performance of the model compared to VAR analogues intended for individual countries. The model is based on the deviations of the variables from their potential values, which enhances GDP growth forecasts compared to a non-detrended design. A wide range of macroeconomic and financial indicators in the model makes the forecast of overall inflation more accurate against simpler benchmarks.

Suggested Citation

  • Olga Korotkikh, 2020. "A Multi-Country BVAR Model for the External Sector," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 98-112, December.
  • Handle: RePEc:bkr:journl:v:79:y:2020:i:4:p:98-112
    DOI: 10.31477/rjmf.202004.98
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/57c/Multi-Country-BVAR-Model.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.31477/rjmf.202004.98?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
    ---><---

    References listed on IDEAS

    as
    1. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    2. Richard H. Clarida & Diane Coyle, 1984. "Conditional Projection by Means of Kalman Filtering," Cowles Foundation Discussion Papers 702, Cowles Foundation for Research in Economics, Yale University.
    3. Holston, Kathryn & Laubach, Thomas & Williams, John C., 2017. "Measuring the natural rate of interest: International trends and determinants," Journal of International Economics, Elsevier, vol. 108(S1), pages 59-75.
    4. Martin Mandler & Michael Scharnagl & Ute Volz, 2022. "Heterogeneity in Euro Area Monetary Policy Transmission: Results from a Large Multicountry BVAR Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 627-649, March.
    5. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    7. Lenza, Michele & Slacalek, Jiri, 2018. "How does monetary policy affect income and wealth inequality? Evidence from quantitative easing in the euro area," Working Paper Series 2190, European Central Bank.
    8. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    9. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    10. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    11. 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.
    12. Angela Capolongo & Claudia Pacella, 2021. "Forecasting inflation in the euro area: countries matter!," Empirical Economics, Springer, vol. 61(5), pages 2477-2499, November.
    13. di Mauro, Filippo & Pesaran, M. Hashem (ed.), 2013. "The GVAR Handbook: Structure and Applications of a Macro Model of the Global Economy for Policy Analysis," OUP Catalogue, Oxford University Press, number 9780199670086.
    14. Geweke, John & Koop, Gary & van Dijk, Herman (ed.), 2013. "The Oxford Handbook of Bayesian Econometrics," OUP Catalogue, Oxford University Press, number 9780199681334.
    15. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    16. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England working papers 583, Bank of England.
    17. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
    18. Ute Volz & Martin Mandler & Michael Scharnagl, 2016. "Heterogeneity in Euro Area Monetary Policy Transmission: Results from a large Multi-Country BVAR," EcoMod2016 9609, EcoMod.
    19. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    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. Jackson, Laura E. & Owyang, Michael T. & Zubairy, Sarah, 2018. "Debt and stabilization policy: Evidence from a Euro Area FAVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 67-91.
    2. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    3. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    4. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    5. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    6. Richard K. Crump & Stefano Eusepi & Domenico Giannone & Eric Qian & Argia M. Sbordone, 2021. "A Large Bayesian VAR of the United States Economy," Staff Reports 976, Federal Reserve Bank of New York.
    7. Higgins, Patrick & Zha, Tao & Zhong, Wenna, 2016. "Forecasting China's economic growth and inflation," China Economic Review, Elsevier, vol. 41(C), pages 46-61.
    8. Hanck, Christoph & Prüser, Jan, 2016. "House prices and interest rates: Bayesian evidence from Germany," Ruhr Economic Papers 620, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
    10. repec:hal:spmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
    11. Anastasios Evgenidis & Apostolos Fasianos, 2021. "Unconventional Monetary Policy and Wealth Inequalities in Great Britain," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 115-175, February.
    12. Brent Meyer & Saeed Zaman, 2019. "The usefulness of the median CPI in Bayesian VARs used for macroeconomic forecasting and policy," Empirical Economics, Springer, vol. 57(2), pages 603-630, August.
    13. Evgenidis, Anastasios & Hamano, Masashige & Vermeulen, Wessel N., 2021. "Economic consequences of follow-up disasters: Lessons from the 2011 Great East Japan Earthquake," Energy Economics, Elsevier, vol. 104(C).
    14. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    15. Pestova, Anna & Mamonov, Mikhail, 2019. "Should we care? : The economic effects of financial sanctions on the Russian economy," BOFIT Discussion Papers 13/2019, Bank of Finland, Institute for Economies in Transition.
    16. repec:zbw:bofitp:2019_013 is not listed on IDEAS
    17. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    18. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    19. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    20. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    21. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    22. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.

    More about this item

    Keywords

    multi-country model; Bayesian methods; conditional forecasting; VAR model;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:bkr:journl:v:79:y:2020:i:4:p:98-112. 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: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    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.