Quantifying the economic response to COVID-19 mitigations and death rates via forecasting Purchasing Managers’ Indices using Generalised Network Autoregressive models with exogenous variables (with discussion)
File(s)
Author(s)
Nason, Guy
Wei, James
Type
Journal Article
Abstract
Knowledge of the current state of economies, how they respond to COVID-19 mitigations and indicators, and what the future might hold for them is important. We use recently-developed generalised network autoregressive (GNAR) models, using trade-determined networks, to model and forecast the Purchasing Managers’ Indices for a number of countries. We use networks that link countries where the links themselves, or their weights, are determined by the degree of export trade between the countries. We extend these models to include node-specific time series exogenous variables (GNARX models), using this to incorporate COVID-19 mitigation stringency indices and COVID-19death rates into our analysis. The highly parsimonious GNAR models considerably out-perform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predicts the extent to which that the UK economy will be affected by harsher, weaker or no interventions.
Date Issued
2022-10-01
Date Acceptance
2021-07-13
Citation
Journal of the Royal Statistical Society Series A: Statistics in Society, 2022, 185 (4), pp.1778-1792
ISSN
0964-1998
Publisher
Royal Statistical Society
Start Page
1778
End Page
1792
Journal / Book Title
Journal of the Royal Statistical Society Series A: Statistics in Society
Volume
185
Issue
4
Copyright Statement
© 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Publication Status
Published
Date Publish Online
2022-11-18