IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/105-ec-2015.html
   My bibliography  Save this paper

Forecasting Russian Macroeconomic Indicators with BVAR

Author

Listed:
  • Boris B. Demeshev

    () (National Research University Higher School of Economics)

  • Oxana A. Malakhovskaya

    () (National Research University Higher School of Economics)

Abstract

This paper evaluates the forecast performance of Bayesian vector autoregressions (BVARs) on Russian data. We estimate BVARs of different sizes and compare the accuracy of their out-ofsample forecasts with those obtained with unrestricted vector autoregressions and random walk with drift. We show that many Russian macroeconomic indicators can be forecast by BVARs more accurately than by competing models. However, contrary to several other studies, we do not confirm that the relative forecast error monotonically decreases with increasing the crosssectional dimension of the sample. In half of those cases where a BVAR appears to be the most accurate model, a small-dimensional BVAR outperforms its high-dimensional counterpart.

Suggested Citation

  • Boris B. Demeshev & Oxana A. Malakhovskaya, 2015. "Forecasting Russian Macroeconomic Indicators with BVAR," HSE Working papers WP BRP 105/EC/2015, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:105/ec/2015
    as

    Download full text from publisher

    File URL: http://www.hse.ru/data/2015/11/10/1078468762/105EC2015.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. Kim, Soyoung & Roubini, Nouriel, 2000. "Exchange rate anomalies in the industrial countries: A solution with a structural VAR approach," Journal of Monetary Economics, Elsevier, vol. 45(3), pages 561-586, June.
    3. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    4. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    5. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    6. repec:wly:japmet:v:25:y:2010:i:1:p:71-92 is not listed on IDEAS
    7. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Papers (Old Series) 1128, Federal Reserve Bank of Cleveland.
    8. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    9. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    2. Fokin, Nikita & Polbin, Andrey, 2019. "A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth," MPRA Paper 95306, University Library of Munich, Germany, revised Apr 2019.
    3. repec:nea:journl:y:2017:i:33:p:75-115 is not listed on IDEAS
    4. Pestova, Anna (Пестова, Анна) & Mamonov, Mikhail (Мамонов, Михаил), 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy
      [Оценка Влияния Различных Шоков На Д
      ," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.

    More about this item

    Keywords

    VAR; BVAR; forecasting; Bayesian estimation;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hig:wpaper:105/ec/2015. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamil Abdulaev) or (Shamil Abdulaev). General contact details of provider: http://edirc.repec.org/data/hsecoru.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.