Kuldeep Kumar's First Contribution to the ‘First Discussion Meeting on Statistical Aspects of the Covid-19 Pandemic’
该研究利用贸易网络构建的广义网络自回归模型，预测多国采购经理指数，并纳入新冠防控严格程度和死亡率等外部变量，发现该模型在预测误差上优于传统向量自回归模型。
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-19 death rates into our analysis. The highly parsimonious GNAR models considerably outperform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform