Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample t can be a poor guide to actual forecasting e ffectiveness. But post-sample model testing requires an often-consequential a priori partitioning of the data into an `in-sample' period...

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an "in-sample"...