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

Author

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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
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    File URL: http://www.cbr.ru/Content/Document/File/87566/wps_8_e.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. repec:bof:bofitp:urn:nbn:fi:bof-201506091268 is not listed on IDEAS
    3. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
    4. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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]
      ," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 48-75, August.

    More about this item

    Keywords

    GDP short-term estimation; nowcast; dynamic factor models.;

    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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