FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*
基于广义菲利普斯曲线，采用动态模型平均法预测美国季度通胀，该方法允许系数和预测模型随时间变化，相比基准回归和时变系数模型显著提升预测精度，并揭示各时期相关预测变量。
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.