Factor models are used to condense high dimensional data consisting of many variables into a much smaller number of factors. The factors represent the comovement between the single time series or underlying nonobserved variables influencing the observations. Here we consider (static and dynamic)

• Principal component models
• Frisch or idiosyncratic noise models
• Generalized linear factor models