Time series factor models

Abstract In today's data‐driven age observing a large number of variables over time for purposes of understanding the dynamics of a system is commonplace. The number of variables grows as our ability to measure, store, and retrieve data grows. Time series factor models and dynamic factor models combine data reduction and time series forecasting strategies to extract the critical information useful in forecasting a singular or small number of series from a large number of explanatory variables. Since the 1990s, these methodologies have proven a useful analytical tool in many areas of applied statistics, but most notably econometrics and finance. Furthermore, recent results in random matrix theory, which examines dimension reduction strategies as the number of variables and observations both go to infinity, have direct applicability in the area of time series factor models. This article reviews the recent history of time series factor models and demonstrates the applicability of these tools, while providing some cautionary issues through an example in the equities market. WIREs Comput Stat 2013, 5:97–104. doi: 10.1002/wics.1245 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data