Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models
评估了代理人使用卡尔曼滤波更新的小型预测模型形成预期的中等规模DSGE模型的经验表现，发现适应性学习模型比理性预期模型更好地拟合数据，且关于通胀持续性的信念解释了通胀均值和波动率的下降以及菲利普斯曲线平坦化。
This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE) model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocks that drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations.