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Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

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  • Oren Barkan
  • Jonathan Benchimol
  • Itamar Caspi
  • Eliya Cohen
  • Allon Hammer
  • Noam Koenigstein

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.

Suggested Citation

  • Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers 2011.07920, arXiv.org, revised Feb 2022.
    • Handle: RePEc:arx:papers:2011.07920
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    Cited by:

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    2. 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.
    3. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    4. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    5. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geo-political uncertainties," Papers 2401.00249, arXiv.org.
    6. 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.
    7. 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.

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    More about this item

    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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