This paper develops a method to analyse large cross-sections with non-trivial time dimensions. The method: (i) identifies the number of common shocks in a factor analytic model; (ii) estimates the unobserved common dynamic component; (iii) shows how to test for fundamentality of the common...

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...

This paper analyses output and productivity for 450 US industries from 1958 to 1986. We make the following contributions. (i) We develop a method based on dynamic principal components to identify the number of common shocks to our data set. (ii) We propose a simple method for the estimation of...