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

TL;DR

This paper introduces RaGNAR, a novel network-based forecasting method that significantly improves UK CPI inflation predictions over traditional models, offering greater accuracy, speed, and simplicity for policymakers.

Contribution

The paper develops RaGNAR, a new approach using random network autoregressive processes, demonstrating superior inflation forecasting performance compared to existing models.

Findings

RaGNAR outperforms benchmark models across all forecast horizons.

RaGNAR provides more accurate predictions than the Bank of England's forecasts.

Combining multiple GNAR models enhances forecast robustness.

Abstract

This article forecasts CPI inflation in the United Kingdom using Random Generalised Network Autoregressive (RaGNAR) Processes. More specifically, we fit Generalised Network Autoregressive (GNAR) Processes to a large set of random networks generated according to the Erd\H{o}s-R\'enyi-Gilbert model and select the best-performing networks each month to compute out-of-sample forecasts. RaGNAR significantly outperforms traditional benchmark models across all horizons. Remarkably, RaGNAR also delivers materially more accurate predictions than the Bank of Englan's four to six month inflation rate forecasts published in their quarterly Monetary Policy Reports. Our results are remarkable not only for their accuracy, but also because of their speed, efficiency and simplicity compared to the Bank's current forecasting processes. RaGNAR's performance improvements manifest both in terms of their root mean squared error and mean absolute percentage error, which measure different, but crucial, aspects of the methods' performance. GNAR processes demonstrably predict future changes to CPI inflation more accurately and quickly than the benchmark models, especially at medium- to long-term forecast horizons, which is of great importance to policymakers charged with setting interest rates. We find that the most robust forecasts are those which combine the predictions from multiple GNAR processes via the use of various model averaging techniques. By analysing the structure of the best-performing graphs, we are also able to identify the key components that influence inflation rates during different periods.