World economies, and especially European ones, have become strongly interconnected in the last decades and a joint modelling is required. We propose here the use of Copulas to build flexible multivariate distributions, since they allow for a rich dependence structure and more flexible marginal...

We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, cross-country dynamic interactions, and country-specific variables. The model provides good forecasting accuracy with respect to the popular benchmarks used in the literature. We...

We compare direct forecasts of HICP and HICP excluding energy and food in the euro area and five member countries to aggregated forecasts of their main components from large Bayesian VARs with a shared set of predictors. We focus on conditional point and density forecasts, in line with...

Density forecast combinations are examined in real-time using the log score to compare five methods: fixed weights, static and dynamic prediction pools, as well as Bayesian and dynamic model averaging. Since real-time data involves one vintage per time period and are subject to revisions, the...

Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECB's monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of...

Putting a price on carbon - with taxes or developing carbon markets - is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the...

Energy inflation is a major source of headline inflation volatility and forecast errors, therefore it is critical to model it accurately. This paper introduces a novel suite of Bayesian VAR models for euro area HICP energy inflation, which adopts a granular, bottom-up approach - disaggregating...

This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components...

In this study, we build two forecasting models to predict inflation Harmonised Index of Consumer Prices (HICP) for the Netherlands and for the euro area. The models provide point forecasts and prediction intervals for both the components of the HICP and the aggregated HICP-index itself. Both...

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...