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Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model

Author

Listed:
  • Alexey Porshakov

    () (Bank of Russia, Russian Federation)

  • Elena Deryugina

    () (Bank of Russia, Russian Federation)

  • Alexey Ponomarenko

    () (Bank of Russia, Russian Federation)

  • Andrey Sinyakov

    () (Bank of Russia, Russian Federation)

Abstract

Real-time assessment of quarterly GDP growth rates is crucial for evaluating an economy’s current prospects given that the relevant data are normally subject to substantial delays in publication by the national statistical agencies. Large information sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are characterized by unbalanced data, mixed frequencies, systematic data revisions, as well as a more general curse of dimensionality problem. The latter issues could, however, be practically resolved by means of dynamic factor model-ing, which has recently been recognized as a useful tool to evaluate current economic conditions by means of higher frequency indicators. Our main results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior to other common alternative specifications. At the same time, we empirically show that the arrival of new data seems to consistently improve DFM’s predictive accuracy throughout sequential nowcast vintages. We also intro-duce an analysis of nowcast evolution resulting from the gradual expansion of the dataset of explanatory variables, as well as the framework for estimating contributions of different blocks of predictors into nowcasts of Russian GDP.

Suggested Citation

  • Alexey Porshakov & Elena Deryugina & Alexey Ponomarenko & Andrey Sinyakov, 2015. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Bank of Russia Working Paper Series wps2, Bank of Russia.
  • Handle: RePEc:bkr:wpaper:wps2
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. repec:bkr:journl:v:78:y:2019:i:1:p:19-35 is not listed on IDEAS
    2. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP
      [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]
      ," MPRA Paper 63713, University Library of Munich, Germany.
    3. repec:eee:ecmode:v:69:y:2018:i:c:p:160-168 is not listed on IDEAS
    4. repec:eee:intfor:v:33:y:2017:i:4:p:915-935 is not listed on IDEAS
    5. Yury Achkasov, 2016. "Nowcasting of the Russian GDP Using the Current Statistics: Approach Modification," Bank of Russia Working Paper Series wps8, Bank of Russia.
    6. repec:onb:oenbfi:y:2018:i:q4/18:b:1 is not listed on IDEAS
    7. Evzen Kocenda & Karen Poghosyan, 2018. "Nowcasting real GDP growth with business tendency surveys data: A cross country analysis," KIER Working Papers 1002, Kyoto University, Institute of Economic Research.
    8. 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.
    9. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland, Institute for Economies in Transition.
    10. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    11. repec:ukb:journl:y:2017:i:242:p:5-13 is not listed on IDEAS

    More about this item

    Keywords

    GDP nowcast; dynamic factor models; principal components; Kalman filter; nowcast evolution;

    JEL classification:

    • 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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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