Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

2020/11/16 by Oren Barkan, Jonathan Benchimol, Barkan, Oren +9 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Energy Load and Power Forecasting #FOS: Economics and business #General Economics (econ.GN) #Market Dynamics and Volatility #Stock Market Forecasting Methods #econ.GN #q-fin.EC

paper · pdf · doi:10.48550/arxiv.2011.07920

openalex publication_date 2020/11/16 · arxiv created 2022/02/17 · arxiv updated 2022/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Forecasting UK Consumer Price Inflation with RaGNAR: Random Generalised Network Autoregressive Processes

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