Forecasting with a Bayesian DSGE Model: An Application to the Euro Area

27 Pages Posted: 29 Oct 2004

See all articles by Frank Smets

Frank Smets

European Central Bank (ECB); KU Leuven - Center for Economic Studies

Rafael Wouters

National Bank of Belgium

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Abstract

In monetary policy strategies geared towards maintaining price stability, conditional and unconditional forecasts of inflation and output play an important role. In this article we illustrate how modern sticky-price dynamic stochastic general equilibrium (DSGE) models, estimated using Bayesian techniques, can become an additional useful tool in the forecasting kit of central banks. First, we show that the forecasting performance of such models compares well with a-theoretical vector autoregressions. Moreover, we illustrate how the posterior distribution of the model can be used to calculate the complete distribution of the forecast, as well as various inflation risk measures that have been proposed in the literature. Finally, the structural nature of the model allows computing forecasts conditional on a policy path. It also allows examination of the structural sources of the forecast errors and their implications for monetary policy. Using those tools, we analyse macroeconomic developments in the euro area since the start of EMU.

Suggested Citation

Smets, Frank and Wouters, Rafael, Forecasting with a Bayesian DSGE Model: An Application to the Euro Area. Available at SSRN: https://ssrn.com/abstract=608500

Frank Smets (Contact Author)

European Central Bank (ECB) ( email )

Kaiserstrasse 29
D-60311 Frankfurt am Main
Germany
+49 69 1344 6550 (Phone)
+49 69 1344 6575 (Fax)

KU Leuven - Center for Economic Studies ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

Rafael Wouters

National Bank of Belgium ( email )

Brussels, B-1000
Belgium
+32 2 221 5441 (Phone)
+32 2 221 3162 (Fax)

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