This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

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 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),...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A...

We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run...

This paper illustrates how to handle a sequence of extreme observations-such as those recorded during the COVID-19 pandemic-when estimating a Vector Autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad-hoc strategy of dropping these...