This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic factor model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure...

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 asks two questions. First, can we detect empirically whether the shocks recovered from the estimates of a structural VAR are truly structural? Second, can the problem of non-fundamentalness be solved by considering additional information? The answer to the first question is 'yes' and...

This paper assesses the performance of Bayesian Vector Autoregression (BVAR) for models of different size. We consider standard specifications in the macroeconomic literature based on, respectively, three and eight variables and compare results with those obtained by larger models containing...

This paper analyzes identification conditions, and proposes an estimator, for a dynamic factor model where the idiosyncratic components are allowed to be mutually non-orthogonal. This model, which we call the generalized dynamic factor model, is novel to the literature, and generalizes the...

The Paper uses a large data set, consisting of 447 monthly macroeconomic time series concerning the main countries of the Euro area to simulate out-of-sample predictions of the Euro area industrial production and the harmonized inflation index and to evaluate the role of financial variables in...

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 shows consistency of a two step estimator of the parameters of a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters are first estimated from an OLS on principal components. In the second step, the factors are estimated...

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...