Author(s):	Antonello D'Agostino, Luca Gambetti, Domenico Giannone
Publication Date:	November 2009
Keyword(s):	Forecasting, Inflation, Stochastic Volatility, Time Varying Vector Autoregression
JEL(s):	C32, E37, E47
Programme Areas:	International Macroeconomics
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=7542

The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naïve random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation.