Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and...

This paper describes an algorithm to compute the distribution of conditional forecasts,i.e. projections of a set of variables of interest on future paths of some othervariables, in dynamic systems. The algorithm is based on Kalman filtering methods andis computationally viable for large vector...

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...

We show that two macroeconomic factors have an important predictive content for governmentbond yields and excess returns. These factors are not spanned by the cross-section of yields andare well proxied by economic growth and real interest rates.

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 assesses the role of surveys for the early estimates of GDP in the euro area in a model-based automated procedures which exploits the timeliness of their release. The analysis is conducted using both an historical evaluation and a real time case study on the current conjuncture.

This paper uses a data-set including time series data on macroeconomic variables, loans, deposits and interest rates for the euro area in order to study the features of financial intermediation over the business cycle. We find that stylized facts for aggregate monetary and real variables are re-...

The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coe±cients VAR with Stochastic...