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Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data

Author

Listed:
  • Sergey V. Arzhenovskiy

    (Rostov Regional Division of the Southern Main Branch of the Central Bank of the Russian Federation, Rostov-on-Don, Russian Federation)

Abstract

The article is devoted to modeling and forecasting of the gross domestic product of Russia based on the data of enterprise survey conducted by the Bank of Russia. The purpose was to study the possibility of improving the efficiency and accuracy of GDP forecasting based on the use of these data. The authors used business climate indicators as well as balances of responses to questions about dynamics of output, demand, and prices. The methodology included regression analysis, as well as a combination of factor and regression analysis, both on aggregate data on all economic sectors and on data on individual economic branches. The forecasting results for two control samples containing actual observations for the eight quarters of 2017–2018 and the five quarters of 2022–2023 suggested that the use of monitoring data improved forecasting accuracy compared to the ARIMA benchmark model. The lowest estimation errors were obtained for approaches combining factor and regression analysis on aggregate data for all economic sectors or the economy as a whole.

Suggested Citation

  • Sergey V. Arzhenovskiy, 2024. "Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 31-44, February.
    • Handle: RePEc:fru:finjrn:240102:p:31-44
    DOI: 10.31107/2075-1990-2024-1-31-44
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    enterprise monitoring; business climate indicator; factor analysis; regression analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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