A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations
提出一种新的时变参数非因果自回归模型，用于研究美国通胀的演变，该模型在拟合和预测精度上优于传统因果模型和常数参数模型，并可用于估计新凯恩斯菲利普斯曲线。
We study the evolution of U.S. inflation by means of a new noncausal autoregressive model with time‐varying parameters that outperforms the corresponding causal and constant‐parameter noncausal models in terms of fit and forecast accuracy. Our model also beats the unobserved component stochastic volatility (UCSV) model, one of the best‐performing univariate inflation forecasting models, in terms of both point and density forecasts. We also show how the new Keynesian Phillips curve can be estimated based on our noncausal model. Both expected and lagged inflation turn out important, but the former dominates in determining the current inflation.