This Paper uses annual data spanning 1870 to 1930 on a set of variables correlated with business conditions to construct an index of real economic activity in Switzerland. We extract an estimate of the common component of the data series using principal components analysis and the unobservable...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and...

The estimation of structural dynamic factor models (DFMs) for large sets of variables is attracting considerable attention. In this paper we briefly review the underlying theory and then compare the impulse response functions resulting from two alternative estimation methods for the DFM....

We present a model of equity trading with informed and uninformed investors where informed investors act upon firm-specific private information and marketwide private information. The model is used to structurally identify the component of order flow that is due to marketwide private...

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. In particular, we focus on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and...

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...

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...