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Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

Author

Listed:
  • Oren Barkan

    (Ariel University)

  • Jonathan Benchimol

    (Bank of Israel)

  • Itamar Caspi

    (Bank of Israel)

  • Allon Hammer

    (Tel-Aviv University)

  • Noam Koenigstein

    (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 on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific prices.

Suggested Citation

  • Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
  • Handle: RePEc:boi:wpaper:2021.06
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    References listed on IDEAS

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    Cited by:

    1. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.

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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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