Forecasting in the absence of precedent
综述了新冠疫情期间宏观经济预测的方法，强调在缺乏历史先例时依赖模型外信息、透明假设和灵活调整的重要性，并用时变参数VAR模型分析发现经济波动主要源于波动性增大而非冲击类型变化。
Abstract We survey approaches to macroeconomic forecasting during the COVID‐19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real‐time forecasts and updating forecasts as new data were observed. We revisit these themes with a time‐varying parameter (TVP) vector autoregression (VAR), which attributes the large jumps primarily to increased volatility rather than changes in the type or propagation of shocks.