Article
Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks
International Journal of Forecasting
(2023)
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
- nflation Forecasting,
- Disaggregated Inflation,
- Consumer Price Index,
- Machine Learning,
- Gated Recurrent Unit,
- Recurrent Neural Networks
Disciplines
Publication Date
September 1, 2023
DOI
10.1016/j.ijforecast.2022.04.009
Citation Information
Jonathan Benchimol, Oren Barkan, Itamar Caspi, Eliya Cohen, et al.. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks" International Journal of Forecasting Vol. 39 Iss. 3 (2023) p. 1145 - 1162 Available at: http://works.bepress.com/jonathanbenchimol/17/