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...

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...

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...

The resurgence of the pandemic and renewed lockdowns have slowed the recovery of the global economy, but the overall losses will be less severe than after the first coronavirus wave in spring 2020. Industry in particular continues to develop well. In contrast, retail trade and personal services...

By using the Economic Sentiment Indicator and Autoregressive Markov Switching models, this paper provides an effective tool to identify and characterize expectations of business cycle phases for Germany, Spain, the Euro Area, and the European Union. This information is useful for policy makers...

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...