Macroeconomic forecasting during recessions and expansions in the US and the euro area
系统评估了11种DSGE和2种BVAR模型在美国和欧元区衰退与扩张期的预测表现，发现没有单一模型占优，预测效果取决于经济状态、变量、期限和评估指标，建议采用多样化的模型组合。
Abstract This study systematically evaluates forecasting performance of 11 Dynamic Stochastic General Equilibrium (DSGE) and 2 Bayesian Vector Autoregression (BVAR) models during recessions and expansions in the US and the euro area. Results show that no single model dominates: parsimonious models perform well in stable periods and at short horizons, while richer DSGE specifications with financial frictions, flexible inflation targeting, or labor market dynamics improve forecasts during recessions. BVARs excel in interest rate forecasting, especially in expansions. Crisis‐specific extensions, such as COVID‐related shocks, yield temporary gains. Forecast accuracy depends on the economic state, variable, horizon, and evaluation metric, underscoring the need for a diversified, context‐dependent modeling toolkit.