Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

TL;DR

This paper introduces a hierarchical RNN model that leverages the CPI hierarchy to improve predictions of disaggregated inflation components, outperforming traditional baselines on US CPI data.

Contribution

The paper proposes a novel Hierarchical Recurrent Neural Network architecture that uses hierarchical information to enhance inflation component forecasts.

Findings

HRNN significantly outperforms baseline models

Utilizes hierarchical data to improve volatile component predictions

Demonstrates effectiveness on US CPI-U index data

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.