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...

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced...

Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three "Vs": the large number of time series continuously...

We compare real-time density forecasts for the euro area using three DSGE models. The benchmark is the Smets-Wouters model and its forecasts of real GDP growth and inflation are compared with those from two extensions. The first adds financial frictions and expands the observables to include a...

This paper compares within-sample and out-of-sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets and Wouters model is the chosen laboratory using quarterly real-time euro area data vintages, covering 2001Q1-2019Q4. The adaptive learning model...

We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse...

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-step ahead forecasts. In the empirical...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB's SPF and Consensus Economics compared to their...