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Nowcasting of the Russian GDP Using the Current Statistics: Approach Modification

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  • Yury Achkasov

    (Bank of Russia, NRU HSE, Russian Federation)

Abstract

This work presents a modification of the model of GDP short-term estimation based on current macroeconomic statistics initially offered in the paper titled 'Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model' by Alexey Porshakov and co-authors [8]. The model modification presented in this work considers factors separately for each of the three groups of indicators - agents' expectations and their estimate of the current economic situation; financial variables, world market and foreign economic activity indicators; real sector indicators. This model can be used to get GDP estimates for the previous and current quarters, which allows researchers to obtain information on output dynamics in the economy in addition to estimates under other models and expert judgments. Also, the model helps decompose GDP quarterly growth rates into various factors.

Suggested Citation

  • Yury Achkasov, 2016. "Nowcasting of the Russian GDP Using the Current Statistics: Approach Modification," Bank of Russia Working Paper Series wps8, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps8
    as

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    References listed on IDEAS

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    1. repec:zbw:bofitp:urn:nbn:fi:bof-201506091268 is not listed on IDEAS
    2. repec:zbw:bofitp:2015_019 is not listed on IDEAS
    3. Belviso Francesco & Milani Fabio, 2006. "Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46, December.
    4. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    5. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    7. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
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    Citations

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    Cited by:

    1. Pestova, Anna, 2020. "“Credit view” on monetary policy in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 72-88.
    2. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    3. Mikhail E. MAMONOV, Anna A. PESTOVA, Vera PANKOVA, Renat Akhmetov, 2020. "Digital Transformation of Capital Market Infrastructure [Цифровая Трансформация Инфраструктуры Рынка Капитала]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 130-159, November.
    4. Pestova, Anna A. (Пестова, Анна) & Mamonov, Mikhail E. (Мамонов, Михаил) & Rostova, Natalia A. (Ростова, Наталья), 2019. "Monetary Policy Shocks in the Russian Economy and Their Macroeconomic Effects [Шоки Процентной Политики Банка России И Оценка Их Макроэкономических Эффектов]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 48-75, August.

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    More about this item

    Keywords

    GDP short-term estimation; nowcast; dynamic factor models.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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