We define nowcasting as the prediction of the present, the very near future and the very recent past. Key in this process is to use timely monthly information in order to nowcast quarterly variables that are published with long delays. We argue that the nowcasting process goes beyond the simple...

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 bridging...

This paper formalizes the process of updating the nowcast and forecast on output and inflation 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...

Not so much and we should not, at least not yet.

We analyse the panel of the Greenbook forecasts (sample 1970-96) and a large panel of monthly variables for the US (sample 1970-2003) and show that the bulk of dynamics of both the variables and their forecasts is explained by two shocks. Moreover, a two factor model which exploits, in real...

This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions. We use estimation technology that facilitates non-nested model comparisons and use a long data set...

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...

The ability of popular statistical methods, the Federal Reserve Greenbook and the Survey of Professional Forecasters to improve upon the forecasts of inflation and real activity from naive models has declined significantly during the most recent period of greater macroeconomic stability. The...

This paper documents a new stylized fact of the U.S. greater macroeconomic stability of the last two decades or so. Using 131 monthly time series, three popular statistical methods and the forecasts of the Federal Reserve's Green book and the Survey of Professional Forecasters, we show that 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...