Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

Oren Barkan (), Jonathan Benchimol, Itamar Caspi, Allon Hammer () and Noam Koenigstein ()
Additional contact information
Oren Barkan: Ariel University
Allon Hammer: Tel-Aviv University
Noam Koenigstein: Tel-Aviv University

No 2021.06, Bank of Israel Working Papers from Bank of Israel

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.

Keywords: Inflation forecasting; Disaggregated inflation; Consumer Price Index; Machine learning; Gated Recurrent Unit; Recurrent Neural Networks (search for similar items in EconPapers)
JEL-codes: C45 C53 E31 E37 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://boiwebrepec.azurefd.net/RePEc/boi/wpaper/WP_2021.06.pdf First version, 2021 (application/pdf)

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)
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