Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323613 
Year of Publication: 
2023
Citation: 
[Journal:] International Journal of Forecasting [ISSN:] 1872-8200 [Volume:] 39 [Issue:] 3 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2023 [Pages:] 1145-1162
Publisher: 
Elsevier, Amsterdam
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.
Subjects: 
Inflation forecasting
Disaggregated inflation
Consumer Price Index
Machine learning
Gated Recurrent Unit
Neural Networks
Data science
Big data
Forecast comparison
Model comparison
JEL: 
C45
C53
E31
E37
C80
C89
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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