Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from...

Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from...

The objective of this paper is to examine the main features of optimal monetary policy cooperation within a micro-founded macroeconometric frame-work. First, using Bayesian techniques, we estimate a two-country dynamic stochastic general equilibrium (DSGE) model for the United States (US) and...

We run a real exchange rate forecasting "horse race", which highlights that two principles hold. First, forecasts should not replicate the high volatility of exchange rates observed in sample. Second, models should exploit the mean reversion of the real exchange rate over long horizons. Abiding...

We use a version of the New Area-Wide Model (NAWM) developed at the ECB in order to quantify the gains from monetary policy cooperation. The model is calibrated in order to match a set of empirical moments. We then derive the cooperative and (open-loop) Nash monetary policies, assuming that the...

This paper compares the Calvo model with a Taylor contracting model in the context of the Smets-Wouters (2003) Dynamic Stochastic General Equilibrium (DSGE) model. In the Taylor price setting model, we introduce firm-specific production factors and discuss how this assumption can help to reduce...

The paper provides new tools for the evaluation of DSGE models, and applies it to a large-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model with price and wage stickiness and capital accumulation. Specifically, we approximate the DSGE model by a vector autoregression (VAR),...

The paper provides a novel Bayesian methodological framework to estimate structural VAR (SVAR) models with recursive identification schemes that allows for the inclusion of over-identifying restrictions. The proposed framework enables the researcher to (i) elicit the prior on the non-zero...

The paper provides a novel Bayesian methodological framework to estimate structural VAR (SVAR) models with recursive identification schemes that allows for the inclusion of over-identifying restrictions. The proposed framework enables the researcher to elicit the prior on the non-zero...