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Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques

Author

Listed:
  • Denis Shibitov

    (Bank of Russia, Russian Federation)

  • Mariam Mamedli

    (Bank of Russia, Russian Federation)

Abstract
We show, how the forecasting performance of models varies, when certain inaccuracies in the pseudo real-time experiment take place. We consider the case of Russian CPI forecasting and estimate several models on not seasonally adjusted data vintages. Particular attention is paid to the availability of the variables at the moment of forecast: we take into account the release timing of the series and the corresponding release delays, in order to reconstruct the forecasting in real-time. In the series of experiments, we quantify how each of these issues affect the out-of-sample error. We illustrate, that the neglect of the release timing generally lowers the errors. The same is true for the use of seasonally adjusted data. The impact of the data vintages depends on the model and forecasting period. The overall effect of all three inaccuracies varies from 8% to 17% depending on the forecasting horizon. This means, that the actual forecasting error can be significantly underestimated, when inaccurate pseudo real-time experiment is run. We underline the need to take these aspects into account, when the real-time forecasting is considered.

Suggested Citation

  • Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps70
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    File URL: http://cbr.ru/StaticHtml/File/120016/wp-apr21_e.pdf
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    References listed on IDEAS

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    Cited by:

    1. Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2022. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 88-103, March.
    2. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
    3. Oleg Semiturkin & Andrey Shevelev, 2023. "Correct Comparison of Predictive Features of Machine Learning Models: The Case of Forecasting Inflation Rates in Siberia," Russian Journal of Money and Finance, Bank of Russia, vol. 82(1), pages 87-103, March.

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    More about this item

    Keywords

    inflation; pseudo real-time forecasting; data vintages; machine learning; neural networks.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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