Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation

This paper develops a predictive approach to Granger causality testing that utilizes k-fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on Granger causality between in ation and unemployment rates.