Does information help recovering structural shocks from past observations?

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 nonfundamentalness be solved by considering additional information? The answer to the first question is “yes” and that to the second is “under some conditions”. JEL Classification: C32, C33, E00, E32, O3C33, E00, E32, JEL Classification: C32, O3

Does information help recovering fundamental structural shocks from past observations?

This paper asks two questions. First, can we detect empirically whether the shocks recovered from the estimates of a structural VAR are fundamental? Second, can the problem of non-fundamentalness be solved by considering additional information? The answer to the firrst question is 'yes' and that to the second is 'under some conditions'.

Trends and cycles in the euro area: how much heterogeneity and should we worry about it?

Not so much and we should not, at least not yet. JEL Classification: E32, C33, C53, F2, F43euro area, European Integration, Income Insurance, International Business Cycles, Risk Sharing

Trends and cycles in the Euro Area: how much heterogeneity and should we worry about it?

Not so much and we should not, at least not yet.International Business Cycles, Euro Area, Risk Sharing, European Integration, Income Insurance.

Nowcasting

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long delays. Until recently, nowcasting had received very little attention by the academic literature, although it was routinely conducted in policy institutions either through a judgemental process or on the basis of simple models. We argue that the nowcasting process goes beyond the simple production of an early estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable. We design a statistical model which produces a sequence of nowcasts in relation to the real time releases of various economic data. The methodology allows to process a large amount of information, as it is traditionally done by practitioners using judgement, but it does it in a fully automatic way. In particular, it provides an explicit link between the news in consecutive data releases and the resulting forecast revisions. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008. JEL Classification: E52, C53, C33factor model, forecasting, news, Nowcasting

A Two-step estimator for large approximate dynamic factor models based on Kalman filtering

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 via the kalman smoother. This projection allows to consider dynamics in the factors and heteroskedasticity in the idiosyncratic variance. The analysis provides theoretical backing for the estimator considered in Giannone, Reichlin, and Sala (2004) and Giannone, Reichlin,and Small (2005).Factor Models, Kalman filter, principal components, large cross-sections