This paper formalizes the process of updating the nowcast and forecast on output and inʿation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving...

This paper evaluates models that exploit timely monthly releases to compute early estimates of current quarter GDP (now-casting) in the euro area. We compare traditional methods used at institutions with a new method proposed by Giannone, Reichlin, and Small (2005). The method consists in...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

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...