IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v78y2019i2p67-93.html
   My bibliography  Save this article

Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model

Author

Listed:
  • Nikita Fokin

    (Russian Presidential Academy of National Economy and Public Administration)

  • Andrey Polbin

    (Russian Presidential Academy of National Economy and Public Administration, Gaidar Institute for Economic Policy)

Abstract

This paper examines an application of the VAR-LASSO model to Russia's key macroeconomic indicators: GDP, household consumption, fixed asset investment, exports, imports, and the rouble real exchange rate, along with oil prices (as an exogenous variable). The slowdown in the Russian economy following the 2008–2009 crisis is modelled as a structural break in the unconditional mean of growth rates of the time series under examination. The model is estimated with the assumption of a common growth rate for GDP, consumption, investment, exports and imports (any discrepancies in actual growth rates are due to changing oil prices and other shocks), which provides a solid foundation for balanced medium-term forecasts using an econometric specification that factors in this constraint. The model exhibits fairly good predictive power when pseudo real-time forecasts are benchmarked against the forecast by the Ministry of Economic Development and the forecast given by the BVAR model in Pestova and Mamonov (2016b), as well as against the best (based on the BIC criterion) VAR(1) model and the classical ARIMA model. The estimated model is used to study functions for impulse responses to oil price shocks and to build scenario-driven forecasts for 2019–2024.

Suggested Citation

  • Nikita Fokin & Andrey Polbin, 2019. "Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model," Russian Journal of Money and Finance, Bank of Russia, vol. 78(2), pages 67-93, June.
    • Handle: RePEc:bkr:journl:v:78:y:2019:i:2:p:67-93
    DOI: 10.31477/rjmf.201901.67
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/f21/RJMF_78-02_ENG_Fokin.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31477/rjmf.201901.67?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. 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.
    2. 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.
    3. Roland Beck & Annette Kamps & Elitza Mileva, 2007. "Long-term growth prospects for the Russian economy," Occasional Paper Series 58, European Central Bank.
    4. Hsu, Nan-Jung & Hung, Hung-Lin & Chang, Ya-Mei, 2008. "Subset selection for vector autoregressive processes using Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3645-3657, March.
    5. 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.
    6. Ivashchenko, S., 2013. "Dynamic Stochastic General Equilibrium Model with Banks and Endogenous Defaults of Firms," Journal of the New Economic Association, New Economic Association, vol. 19(3), pages 27-50.
    7. Skrobotov, Anton (Скроботов, Антон) & Fokin, Nikita (Фокин, Никита), 2018. "Testing the Asymmetric Convergence of the Real Exchange Rate to Equilibrium During the Managed Ruble Exchange Rate Regime [Тестирование Ассиметричной Сходимости Реального Обменного Курса К Равновес," Working Papers 021808, Russian Presidential Academy of National Economy and Public Administration.
    8. A. Pestova & M. Mamonov., 2016. "A survey of methods for macroeconomic forecasting: Looking for perspective directions in Russia," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    9. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, vol. 30(1), pages 1-11.
    10. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    11. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    12. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-179, April.
    13. Beck, Roland & Kamps, Annette & Mileva, Elitza, 2007. "Long-term growth prospects for the Russian economy," Occasional Paper Series 58, European Central Bank.
    14. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    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. Fokin, Nikita (Фокин, Никита), 2019. "VAR-LASSO model for the Russian economy on a large data set [Var-Lasso Модель Для Российской Экономики На Большом Массиве Данных]," Working Papers 031951, Russian Presidential Academy of National Economy and Public Administration.
    17. Deryugina, Elena & Ponomarenko, Alexey, 2014. "A large Bayesian vector autoregression model for Russia," BOFIT Discussion Papers 22/2014, Bank of Finland Institute for Emerging Economies (BOFIT).
    18. Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
    19. Bela Balassa, 1964. "The Purchasing-Power Parity Doctrine: A Reappraisal," Journal of Political Economy, University of Chicago Press, vol. 72, pages 584-584.
    20. Ivashchenko, S., 2013. "Dynamic Stochastic General Equilibrium Model with Banks and Endogenous Defaults of Firms," Journal of the New Economic Association, New Economic Association, vol. 19(3), pages 27-50.
    21. A. Polbin., 2017. "Modeling the real ruble exchange rate under monetary policy regime change," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 4.
    22. O. Malakhovskaya., 2016. "DSGE-based forecasting: What should our perspective be?," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 12.
    23. Skrobotov, Anton A. (Скроботов, Антон) & Fokin, Nikita D. (Фокин, Никита), 2018. "Testing Asymmetric Convergence of the Real Exchange Rate to Equilibrium During Ruble Exchange Rate Targeting [Тестирование Асимметричной Сходимости Реального Обменного Курса К Равновесному Во Время," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, pages 132-147, June.
    24. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
    25. 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.
    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. Mikhail Gareev, 2020. "Use of Machine Learning Methods to Forecast Investment in Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 35-56, March.

    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 & 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. 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. Polbin, Andrey & Shumilov, Andrei & Bedin, Andrei & Kulikov, Alexander, 2019. "Modeling real exchange rate of the Russian ruble using Markov regime switching approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 32-50.
    4. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    5. 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.
    6. 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.
    7. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    8. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    9. Nuutilainen, Riikka & Rautava, Jouko, 2020. "Russia and the slowdown of the Chinese economy," BOFIT Policy Briefs 2/2020, Bank of Finland Institute for Emerging Economies (BOFIT).
    10. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    11. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
    12. Polbin, Andrey, 2017. "Моделирование Реального Курса Рубля В Условиях Изменения Режима Денежно-Кредитной Политики [Modeling the real ruble exchange rate under monetary policy regime change]," MPRA Paper 78139, University Library of Munich, Germany.
    13. Zachary F. Fisher & Younghoon Kim & Barbara L. Fredrickson & Vladas Pipiras, 2022. "Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 1-29, June.
    14. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    15. Mikhail Mamonov & Vera Pankova & Renat Akhmetov & Anna Pestova, 2020. "Financial Shocks and Credit Cycles," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 45-74, December.
    16. A. Polbin., 2017. "Modeling the real ruble exchange rate under monetary policy regime change," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 4.
    17. Schnücker, A.M., 2019. "Penalized Estimation of Panel Vector Autoregressive Models," Econometric Institute Research Papers EI-2019-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Maiorova, Ksenia & Fokin, Nikita, 2020. "Наукастинг Темпов Роста Стоимостных Объемов Экспорта И Импорта По Товарным Группам [Nowcasting the growth rates of the export and import by commodity groups]," MPRA Paper 109557, University Library of Munich, Germany.
    19. Salmanov, Oleg & Zaernjuk, Victor & Lopatina, Olga & Drachena, Irina & Vikulina, Evgeniya, 2016. "Investigating the Impact of Monetary Policy using the Vector Autoregression Method," MPRA Paper 112280, University Library of Munich, Germany, revised 01 Jun 2016.
    20. Yuen, T.P. & Wong, H. & Yiu, K.F.C., 2018. "On constrained estimation of graphical time series models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 27-52.

    More about this item

    Keywords

    vector autoregression; machine learning; regularised models; GDP forecast; Russian economy; curse of dimensionality; oil prices;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

    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:78:y:2019:i:2:p:67-93. 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.