Noncausal Bayesian Vector Autoregression
研究了非因果向量自回归模型的贝叶斯分析，该模型能捕捉非线性和缺失变量效应，应用于美国战后通胀和GDP增长数据，发现非因果模型在样本内拟合和样本外预测上均优于传统因果模型。
Summary We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by‐product. We apply the methods to postwar US inflation and GDP growth. The noncausal model is found superior in terms of both in‐sample fit and out‐of‐sample forecasting performance over its conventional causal counterpart. Economic shocks based on the noncausal model turn out to be highly anticipated in advance. We also find the GDP growth to have predictive power for future inflation, but not vice versa. Copyright © 2016 John Wiley & Sons, Ltd.