Author Description

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. Contents; I. Introduction; Ii. Related Literature; Iii. Establishing Granger Causality; Iv. Empirical Results; A. Univariate And Bivariate Models; Figure; 1. Data; 2. Difference In Rmse At Different Forecasting Horizon Between Univariate And Bivariate Model; 3. Impulse Response Euro Area Pre-1988 (1970q3-1988q2); 4. Impulse Response Euro Area Post-1988 (1988q3-12006q4); 5. Difference In Rmse At Different Forecasting Horizon Between Univariate And Bivariate Model (diffuse Priors); 6. Difference In Rmse At Different Forecasting Horizon Between Univariate And Bivariate Model 7. Difference In Rmse At Different Forecasting Horizon Between Univariate And Bivariate Model (breakpoint 1993q2)8 Difference In Rmse At Different Forecasting Horizon Between Trivariate And Fourvariate Model; V. Horserace; 9. Rmse For All Models For The Pre- And Post-1988 Subsamples; Vi. Conclusions; References Pär Österholm, Helge Berger. Description Based Upon Print Version Of Record. Includes Bibliographical References. English