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Knowledge of the current state of economies, how they respond to COVID-19mitigations and indicators, and what the future might hold for them isimportant. We use recently-developed generalised network autoregressive (GNAR)models, using trade-determined networks, to model and forecast the PurchasingManagers' Indices for a number of countries. We use networks that linkcountries where the links themselves, or their weights, are determined by thedegree of export trade between the countries. We extend these models to includenode-specific time series exogenous variables (GNARX models), using this toincorporate COVID-19 mitigation stringency indices and COVID-19 death ratesinto our analysis. The highly parsimonious GNAR models considerably outperformvector autoregressive models in terms of mean-squared forecasting error and ourGNARX models themselves outperform GNAR ones. Further mixed frequency modellingpredicts the extent to which that the UK economy will be affected by harsher,weaker or no interventions.

Quantifying the economic response to COVID-19 mitigations and death  rates via forecasting Purchasing Managers' Indices using Generalised Network  Autoregressive models with exogenous variables