This Paper is the result of the Bank of Italy-CEPR project to construct a monthly coincident indicator of the business cycle of the euro area. The index is estimated on the basis of a harmonized data set of monthly statistics of the euro area (951 series) which we constructed from a variety of...

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...

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in SVAR analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness”, which is intractable in structural...

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in structural vector autoregression (SVAR) analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness,”...

This paper shows that temporal aggregation affects estimates of trend-cycle variances and of persistence of shocks to economic variables. The authors analyze UCARIMA models with orthogonal components and show two results. First, they prove that when the decay rates of the autocovariance...

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in SVAR analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness”, which is intractable in structural...

This paper proposes a factor model with infinite dynamics and nonorthogonal idiosyncratic components. The model, which we call the generalized dynamic-factor model, is novel to the literature and generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the...

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

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...