Does money growth Granger-cause inflation in the euro area?: evidence from out-of-sample forecasts using Bayesian VARs, Issues 2008-2053
International Monetary Fund, 2008 - Business & Economics - 29 pages
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.
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Establishing Granger Casuality
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95 percent prior B)VAR BAR BVAR BAR bivariate BVAR model Bivariate BVAR Univariate Bivariate Univariate Bivariate bivariate versus univariate breakpoint BVAR BAR BVAR BVAR Univariate BAR central bank contribution of money covariance matrix Diebold and Mariano difference in RMSE different forecasting horizon diffuse priors disinflation DM test statistics ECB's empirical euro area Figure forecasting accuracy forecasting performance fourvariate model full sample Granger causality growth rates horizon in BVAR horizon in quarters impulse response indicates significance inflation and money inflation target interest rate late subsample M3-based model Mariano test mean square error monetary aggregates monetary policy out-of-sample forecasting percent level percent prior probability positive marginal contribution post-1988 subsamples pre-1988 subsample prior probability interval real GDP growth RMSE at different RMSE based Root mean square sample period standard-normal approximation statistical relevance steady-state priors steady-state values univariate and bivariate univariate BAR model Univariate Bivariate Univariate variables versus univariate model Wicksellian