IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0424.html
   My bibliography  Save this article

The importance of modeling structural breaks in forecasting Russian GDP

Author

Listed:
  • Fokin, Nikita

    (RANEPA, Moscow, Russian Federation)

Abstract

The paper considers two types of models for forecasting seasonally adjusted Russian GDP under the structural breaks. Models that allow breaks in a deterministic trend, in which the dates of structural breaks are set exogenously, and more flexible class of models – with a stochastic trend are considered. It is shown that modeling a structural break in a deterministic trend or adding a stochastic trend significantly improves the quality of 3–4 steps ahead forecasts, and sometimes even on shorter horizons, compared to models with a constant trend growth rate.

Suggested Citation

  • Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    • Handle: RePEc:ris:apltrx:0424
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2021_63_005-029.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    2. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    3. 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.
    4. Skrobotov, Anton, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    5. Tara M. Sinclair, 2009. "The Relationships between Permanent and Transitory Movements in U.S. Output and the Unemployment Rate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2-3), pages 529-542, March.
    6. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    7. A. Kudrin & E. Gurvich., 2014. "A New Growth Model for the Russian Economy," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 12.
    8. Andrey Polbin, 2021. "Multivariate unobserved component model for an oil-exporting economy: the case of Russia," Applied Economics Letters, Taylor & Francis Journals, vol. 28(8), pages 681-685, May.
    9. Basistha, Arabinda & Nelson, Charles R., 2007. "New measures of the output gap based on the forward-looking new Keynesian Phillips curve," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 498-511, March.
    10. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    11. Polbin, Andrey V. (Полбин, Андрей В.), 2020. "Estimating Time-Varying Long-Run Growth Rate of Russian GDP in the ARX Model with Oil Prices [Оценка Траектории Темпов Трендового Роста Ввп России В Arx-Модели С Ценами На Нефть]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 40-63, February.
    12. 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.
    13. Hadi Salehi Esfahani & Kamiar Mohaddes & M. Hashem Pesaran, 2014. "An Empirical Growth Model For Major Oil Exporters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 1-21, January.
    14. Rautava, Jouko, 2004. "The role of oil prices and the real exchange rate in Russia's economy--a cointegration approach," Journal of Comparative Economics, Elsevier, vol. 32(2), pages 315-327, June.
    15. Kuboniwa, Masaaki, 2014. "A comparative analysis of the impact of oil prices on oil-rich emerging economies in the Pacific Rim," Journal of Comparative Economics, Elsevier, vol. 42(2), pages 328-339.
    16. Berger, Tino & Everaert, Gerdie & Vierke, Hauke, 2016. "Testing for time variation in an unobserved components model for the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 179-208.
    17. Idrisov, Georgy & Kazakova, Maria & Polbin, Andrey, 2015. "A theoretical interpretation of the oil prices impact on economic growth in contemporary Russia," Russian Journal of Economics, Elsevier, vol. 1(3), pages 257-272.
    18. Sergey Sinelnikov-Murylev & Sergey Drobyshevsky & Maria Kazakova & Michael Alexeev, 2016. "Decomposition of Russia's GDP Growth Rates," Research Paper Series, Gaidar Institute for Economic Policy, issue 167P, pages 123-123.
    19. Peter K. Clark, 1987. "The Cyclical Component of U. S. Economic Activity," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(4), pages 797-814.
    20. N. Orlova & S. Egiev., 2015. "Structural Factors of Russian Economic Slowdown," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 12.
    21. 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.
    22. Camba-Mendez, Gonzalo, 2012. "Conditional forecasts on SVAR models using the Kalman filter," Economics Letters, Elsevier, vol. 115(3), pages 376-378.
    23. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.
    24. Полбин Андрей Владимирович & Скроботов Антон Андреевич, 2016. "Тестирование Наличия Изломов В Тренде Структурной Компоненты Ввп Российской Федерации," 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 588-623.
    25. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    26. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    27. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
    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. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    2. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    3. Cremaschini, Alessandro & Maruotti, Antonello, 2023. "A finite mixture analysis of structural breaks in the G-7 gross domestic product series," Research in Economics, Elsevier, vol. 77(1), pages 76-90.
    4. Balash, Vladimir & Faizliev, Alexey, 2024. "Volatility spillovers across Russian oil and gas sector. Evidence of the impact of global markets and extraordinary events," Energy Economics, Elsevier, vol. 129(C).

    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. Fokin, Nikita & Haritonova, Marina, 2020. "Сравнительный Анализ Прогнозных Моделей Российского Ввп В Условиях Наличия Структурных Сдвигов [Comparative analysis of the forecasting models for Russia’s GDP under the structural breaks]," MPRA Paper 103412, University Library of Munich, Germany.
    2. Andrey Feliksovich Bedin & Alexander Vladimirovich Kulikov & Andrey Vladimirovich Polbin, 2021. "A Markov Switching VECM Model for Russian Real GDP, Real Exchange Rate and Oil Prices," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 402-412.
    3. 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.
    4. Polbin, Andrey & Skrobotov, Anton, 2017. "Спектральная Оценка Компоненты Бизнес Цикла Ввп России С Учетом Высокой Зависимости От Условий Торговли [Spectral estimation of the business cycle component of the Russian GDP under high dependence," MPRA Paper 78667, University Library of Munich, Germany.
    5. Andrey Polbin, 2021. "Multivariate unobserved component model for an oil-exporting economy: the case of Russia," Applied Economics Letters, Taylor & Francis Journals, vol. 28(8), pages 681-685, May.
    6. Eiji Kurozumi & Anton Skrobotov, 2018. "Confidence Sets for the Break Date in Cointegrating Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(3), pages 514-535, June.
    7. Zubarev, A. & Rybak, K., 2022. "The impact of global shocks on the Russian economy: FAVAR approach," Journal of the New Economic Association, New Economic Association, vol. 56(4), pages 48-68.
    8. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    9. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.
    10. Manuel González-Astudillo & John M. Roberts, 2022. "When are trend–cycle decompositions of GDP reliable?," Empirical Economics, Springer, vol. 62(5), pages 2417-2460, May.
    11. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    12. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    14. 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.
    15. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    16. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    17. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    18. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    19. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 869-889, August.
    20. A. Polbin., 2017. "Econometric estimation of the impact of oil prices shock on the Russian economy in VECM model," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 10.

    More about this item

    Keywords

    forecasting; real GDP; structural breaks; long-term growth rate; oil prices; Russian economy.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:ris:apltrx:0424. 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: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    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.