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 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...

The European Union (EU) is facing the challenge of enlargement to almost double its current size. This change has strong implications for the balance of power among member states. Building on the work of Shapley (1977) and Owen (1972), we present a measure of power that is based on players'...

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

In this paper we present a generalization of power indices which includes the preferences of the voters. Using a Multilinear Extension perspective (Owen, 1972a) we measure the probability of the players' voting 'yes' for a particular political issue. Further, we randomize the issues and show the...

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

This paper considers Bayesian regression with normal and double exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

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...