Imperial College London
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ProfessorGuyNason

Faculty of Natural Sciences, Department of Mathematics

Chair in Statistics
 
 
 
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Contact

 

g.nason Website

 
 
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Location

 

530Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nason:2022:10.1111/rssa.12875,
author = {Nason, G and Wei, J},
doi = {10.1111/rssa.12875},
journal = {Journal of the Royal Statistical Society Series A: Statistics in Society},
pages = {1778--1792},
title = {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)},
url = {http://dx.doi.org/10.1111/rssa.12875},
volume = {185},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Nason,G
AU  - Wei,J
DO  - 10.1111/rssa.12875
EP  - 1792
PY  - 2022///
SN  - 0964-1998
SP  - 1778
TI  - 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)
T2  - Journal of the Royal Statistical Society Series A: Statistics in Society
UR  - http://dx.doi.org/10.1111/rssa.12875
UR  - http://hdl.handle.net/10044/1/90361
VL  - 185
ER  -
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