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

TLDR

Conditional and unconditional inflation and output forecasts are crucial for central banks' price‑stability strategies. The article demonstrates that Bayesian sticky‑price DSGE models can serve as an additional forecasting tool for central banks. The Bayesian DSGE model’s posterior distribution enables full forecast distributions, inflation‑risk metrics, policy‑path‑conditional forecasts, and analysis of structural forecast‑error sources. The model performs comparably to theoretical VARs and is used to analyze euro‑area macro developments since EMU inception.

Abstract

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.