Financial shocks generate a protracted and quantitatively important effect on real economic activity and financial markets only if the shocks are both negative and large. Otherwise, their role is quite modest. Financial shocks have become more important for economic fluctuations after the 2000...

Financial shocks represent a major driver of fluctuations in tail risk, defined as the 5th percentile of the forecast distributions of output and inflation. Since the variance and the asymmetry of the forecast distributions are largely driven by the left tail, financial shocks turn out to play a...

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...

We use a dynamic factor model to provide a semi-structural representation for 101 quarterly US macroeconomic series. We find that (i) the US economy is well described by a number of structural shocks between two and five. Focusing on the four-shock specification, we identify, using sign...

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse response functions. In particular, we argue that, if the data follow an approximate factor...