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Forecasting for the Russian Economy Using Small-Scale DSGE Models

Author

Listed:
  • Dmitry Kreptsev

    (Bank of Russia)

  • Sergei Seleznev

    (Bank of Russia)

Abstract

This study examines the ability of a small-scale DSGE model to forecast the dynamics of key macroeconomic variables for the Russian economy. The study uses two versions of a standard model of a small open economy, adding a stochastic oil price trend under various assumptions about exchange rate policy. Comparison with the same size BVAR model shows DSGE models to be superior as regards exchange rate, price and interest rate forecasting and slightly inferior with respect to GDP forecasting.

Suggested Citation

  • Dmitry Kreptsev & Sergei Seleznev, 2018. "Forecasting for the Russian Economy Using Small-Scale DSGE Models," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 51-67, June.
  • Handle: RePEc:bkr:journl:v:77:y:2018:i:2:p:51-67
    DOI: 10.31477/rjmf.201802.51
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    1. Fernández-Villaverde, Jesús & Gordon, Grey & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Nonlinear adventures at the zero lower bound," Journal of Economic Dynamics and Control, Elsevier, vol. 57(C), pages 182-204.
    2. Polbin, Andrey, 2014. "Econometric estimation of a structural macroeconomic model for the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 3-29.
    3. Schmitt-Grohe, Stephanie & Uribe, Martin, 2003. "Closing small open economy models," Journal of International Economics, Elsevier, vol. 61(1), pages 163-185, October.
    4. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    5. Lubik, Thomas A. & Matthes, Christian, 2016. "Indeterminacy and learning: An analysis of monetary policy in the Great Inflation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 85-106.
    6. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2008. "The new area-wide model of the euro area: a micro-founded open-economy model for forecasting and policy analysis," Working Paper Series 944, European Central Bank.
    7. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    8. S Borağan Aruoba & Pablo Cuba-Borda & Frank Schorfheide, 2018. "Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(1), pages 87-118.
    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. Lan, Hong & Meyer-Gohde, Alexander, 2014. "Solvability of perturbation solutions in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 366-388.
    11. Carl E. Walsh, 2010. "Monetary Theory and Policy, Third Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262013770, April.
    12. Mariano Kulish & James Morley & Tim Robinson, 2014. "Estimating DSGE models with forward guidance," Discussion Papers 2014-32A, School of Economics, The University of New South Wales.
    13. Müller, Ulrich K., 2012. "Measuring prior sensitivity and prior informativeness in large Bayesian models," Journal of Monetary Economics, Elsevier, vol. 59(6), pages 581-597.
    14. 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.
    15. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    16. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England working papers 583, Bank of England.
    17. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    18. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    19. Lilia Maliar & Serguei Maliar, 2015. "Merging simulation and projection approaches to solve high‐dimensional problems with an application to a new Keynesian model," Quantitative Economics, Econometric Society, vol. 6(1), pages 1-47, March.
    20. Roberto Motto & Massimo Rostagno & Lawrence J. Christiano, 2010. "Financial Factors in Economic Fluctuations," 2010 Meeting Papers 141, Society for Economic Dynamics.
    21. Edward P. Herbst & Frank Schorfheide, 2016. "Bayesian Estimation of DSGE Models," Economics Books, Princeton University Press, edition 1, number 10612.
    22. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
    23. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
    24. Julio J. Rotemberg, 1982. "Monopolistic Price Adjustment and Aggregate Output," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(4), pages 517-531.
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    Cited by:

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    2. Mikhail Andreyev & Alyona Nelyubina, 2024. "Energy transition scenarios in Russia: effects in macroeconomic general equilibrium model with rational expectations," Bank of Russia Working Paper Series wps122, Bank of Russia.
    3. Votinov, A., 2022. "The effects of additional non-stationary processes on the properties of DSGE-models," Journal of the New Economic Association, New Economic Association, vol. 55(3), pages 28-43.
    4. Oleg Kryzhanovsky & Alexander Zykov, 2022. "DEMUR: A Regional Semi-Structural Model of the Ural Macroregion," Russian Journal of Money and Finance, Bank of Russia, vol. 81(4), pages 52-85, December.
    5. Henry Penikas, 2023. "Smoothing the Key Rate Pass-Through: What to Keep in Mind When Interpreting Econometric Estimates," Russian Journal of Money and Finance, Bank of Russia, vol. 82(3), pages 3-34, September.
    6. Artur Sharafutdinov, 2023. "Forecasting Russian GDP, Inflation, Interest Rate, and Exchange Rate Using DSGE-VAR Model," Russian Journal of Money and Finance, Bank of Russia, vol. 82(3), pages 62-86, September.
    7. Mikhail Andreyev & Mikhail Andreyev & Mikhail Andreyev, 2020. "Adding a fiscal rule into a DSGE model: How much does it change the forecasts?," Bank of Russia Working Paper Series wps64, Bank of Russia.
    8. Polbin, Andrey & Sinelnikov-Murylev, Sergey, 2024. "Developing and impulse response matching estimation of the DSGE model for the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 5-34.
    9. Dementiev, V., 2023. "Updating the technological base of the economy and real interest rates," Journal of the New Economic Association, New Economic Association, vol. 60(3), pages 104-119.
    10. Bünyamin Fuat Yıldız & Korhan K. Gökmenoğlu & Wing-Keung Wong, 2022. "Analysing Monetary Policy Shocks by Sign and Parametric Restrictions: The Evidence from Russia," Economies, MDPI, vol. 10(10), pages 1-16, September.
    11. V. I. Baluta & D. N. Shul’ts & P. A. Lavrinenko, 2022. "Assessing the Impact of Global Hydrocarbon Prices on the Russian Economy Based on the DSGE Model with Capital-Owning Firms," Studies on Russian Economic Development, Springer, vol. 33(1), pages 107-117, February.
    12. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.

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    More about this item

    Keywords

    Non-stationary DSGE; BVAR; forecasting;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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