Factor models are used to condense high dimensional data consisting of many vari ables into a much smaller number of factors. Here we present an introductory survey to factor models for time series, where the factors represent the comovement between the single time series. Principal component analysis, linear dynamic factor models with idiosyncratic noise and generalized linear dynamic factor models are introduced and structural properties, such as identifiability, as well as estimation are discussed.
"Modelling High-Dimensional Time Series by Generalized Linear Dynamic Factor Models: An Introductory Survey." Commun. Inf. Syst. 7 (2) 153 - 166, 2007.