Time Series Concepts

Abstract

This chapter provides background material on time series concepts that are used throughout the book. These concepts are presented in an informal way, and extensive examples using S-PLUS are used to build intuition. Section 3.2 discusses time series concepts for stationary and ergodic univariate time series. Topics include testing for white noise, linear and autoregressive moving average (ARMA) process, estimation and forecasting from ARMA models, and long-run variance estimation. Section 3.3 introduces univariate nonstationary time series and defines the important concepts of I(0) and I(1) time series. Section 3.4 explains univariate long memory time series. Section 3.5 covers concepts for stationary and ergodic multivariate time series, introduces the class of vector autoregression models, and discusses long-run variance estimation. Rigorous treatments of the time series concepts presented in this chapter can be found in Fuller (1996) and Hamilton (1994). Applications of these concepts to financial time series are provided by Campbell, Lo and MacKin-lay