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. But post-sample model testing requires an often-consequential a priori partitioning of the data into an 'in-sample' period...

The literature on the relationship between real output growth and the growth rate in the price of oil, including an allowance for asymmetry in the impact of oil prices on output, continues to evolve. Here we show that a new technique, which allows us to control for both this asymmetry and also...

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 <em>a priori</em> partitioning of the data into an...

Credible inference requires attention to the possible fragility of the results (p-values for key hypothesis tests) to flaws in the model assumptions, notably including the validity of the instruments used. Past sensitivity analysis has mainly consisted of experimentation with alternative model...