IDEAS home Printed from https://ideas.repec.org/a/hig/ecohse/202245.html
   My bibliography  Save this article

Nowcasting of the Components of Russian GDP

Author

Listed:
  • Natalya Makeeva

    (National Research University Higher School of Economics, Moscow, Russia)

  • Ivan Stankevich

    (National Research University Higher School of Economics, Moscow, Russia)

Abstract

The paper discusses the problem of nowcasting the current growth rates of Russian GDP and its components using quarterly data. The quality of restricted and unrestricted MIDAS models (models with mixed data), MIDAS model with L1 regularisation and MFBVAR model (Bayesian vector autoregression of mixed frequency) are compared. The results are compared with classical autoregression to justify the need to use nowcasting models for the rapid assessment of macroeconomic indicators. Production indices for various industries and macro indicators characterising Russian GDP and its components were used as explanatory variables. The paper proposes a way to quickly assess the current state of the economy and proposes a nowcasting method based on data only for the first or first two months of the quarter under consideration. As a result, for each dependent variable, the best model for building a nowcast based on the last 12 points is selected based on the criterion of mean absolute error (MAE) and root mean square prediction error (RMSE).

Suggested Citation

  • Natalya Makeeva & Ivan Stankevich, 2022. "Nowcasting of the Components of Russian GDP," HSE Economic Journal, National Research University Higher School of Economics, vol. 26(4), pages 598-622.
    • Handle: RePEc:hig:ecohse:2022:4:5
    as

    Download full text from publisher

    File URL: https://ej.hse.ru/en/2022-26-4/800824520.html
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    More about this item

    Keywords

    nowcasting; Russian GDP; time series; mixed frequency models; MIDAS models; forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - 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:hig:ecohse:2022:4:5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Editorial board or Editorial board (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.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.