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Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

Author

Listed:
  • Oren Barkan

    (The Open University of Israel)

  • Jonathan Benchimol

    (BoI - Bank of Israel)

  • Itamar Caspi

    (BoI - Bank of Israel)

  • Eliya Cohen

    (TAU - Tel Aviv University)

  • Allon Hammer

    (TAU - Tel Aviv University)

  • Noam Koenigstein

    (TAU - Tel Aviv University)

Abstract

We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused mainly on predicting the inflation headline, many economic and financial entities are more interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model that utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Our evaluations, based on a large data-set from the US CPI-U index, indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print emse-04624940, HAL.
    • Handle: RePEc:hal:journl:emse-04624940
    DOI: 10.1016/j.ijforecast.2022.04.009
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    Cited by:

    1. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    2. Patricia Toledo & Roberto Duncan, 2024. "Forecasting food price inflation during global crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1087-1113, July.
    3. Jonathan Leslie, 2023. "?Seeing? the Future: Improving Macroeconomic Forecasts with Spatial Data and Recurrent Convolutional Neural Networks," CAEPR Working Papers 2023-003 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    4. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
    5. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    6. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    7. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    8. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
    9. 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.
    10. Fang, Yi & Chen, Yuzhi & Ren, Hang, 2023. "A factor pricing model based on machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 280-297.

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    More about this item

    Keywords

    Inflation Forecasting; Disaggregated Inflation; Consumer Price Index; Machine Learning; Gated Recurrent Unit; Recurrent Neural Networks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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