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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
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    File URL: http://www.hse.ru/data/2015/11/10/1078468762/105EC2015.pdf
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    References listed on IDEAS

    as
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    13. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
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    16. 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.
    17. 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.
    18. Scholl, Almuth & Uhlig, Harald, 2008. "New evidence on the puzzles: Results from agnostic identification on monetary policy and exchange rates," Journal of International Economics, Elsevier, vol. 76(1), pages 1-13, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. Shulgin, A., 2017. "Two-Dimensional Monetary Policy Shocks in DSGE-Model Estimated for Russia," Journal of the New Economic Association, New Economic Association, vol. 33(1), pages 75-115.
    3. 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," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    4. 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.
    5. Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.

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    More about this item

    Keywords

    VAR; BVAR; forecasting; Bayesian estimation;
    All these keywords.

    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

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