Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

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

EconStor Open Access Articles and Book Chapters, 2023, vol. 39, issue 3, 1145-1162

Abstract: We present a hierarchical architecture based on recurrent neural networks 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 price changes.

Keywords: Inflation forecasting; Disaggregated inflation; Consumer Price Index; Machine learning; Gated Recurrent Unit; Neural Networks; Data science; Big data; Forecast comparison; Model comparison (search for similar items in EconPapers)
JEL-codes: C45 C53 C80 C89 E31 E37 (search for similar items in EconPapers)
Date: 2023
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Related works:
Journal Article: Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks (2023)
Working Paper: Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks (2023)
Working Paper: Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks (2022)
Working Paper: Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:323613

DOI: 10.1016/j.ijforecast.2022.04.009

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