Given the increased importance of fiscal monitoring, this study amends the existing literature in the field of intra-annual fi scal data in two main dimensions. First, we use quarterly fi scal data to forecast a very disaggregated set of fiscal series at annual frequency. This makes the analysis...

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...

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...

In a globalised world economy, global factors have become increasingly important to explain trade flows at the expense of country-specific determinants. This paper shows empirically the superiority of direct forecasting methods, in which world trade is directly forecasted at the aggregate...

This paper explores the role of model and vintage combination in forecasting, with a novel approach that exploits the information contained in the revision history of a given variable. We analyse the forecast performance of eleven widely used models to predict inflation and GDP growth, in the...

This paper considers Bayesian regression with normal and double-exponential 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...