We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allows us to shed some light on the practical...

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 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...

We study the workings of the factor analysis of high-dimensional data using arti?cial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allow us to shed some light on the practical bene?ts...

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 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...