We use Bayesian estimation techniques to investigate whether money growth Granger-causes inflation in the United States. We test for Granger-causality out-of-sample and find, perhaps surprisingly given recent theoretical arguments, that including money growth in simple VAR models of inflation does systematically improve out-of-sample forecasting accuracy. This holds for a long forecasting sample 1960-2005, as well for more recent subperiods, including the Volcker and Greenspan eras. However, the contribution of money to inflation forecasting accuracy is quantitatively limited and tends to be smaller in recent subperiods, in particular in models that also include information on real GDP growth and interest rates.
We use a mean-adjusted Bayesian VAR model as an out-of-sample forecasting tool to test whether money growth Granger-causes inflation in the euro area. Based on data from 1970 to 2006 and forecasting horizons of up to 12 quarters, there is surprisingly strong evidence that including money improves forecasting accuracy. The results are very robust with regard to alternative treatments of priors and sample periods. That said, there is also reason not to overemphasize the role of money. The predictive power of money growth for inflation is substantially lower in more recent sample periods compared to the 1970s and 1980s. This cautions against using money-based inflation models anchored in very long samples for policy advice.