Noncausal Bayesian vector autoregression

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.