After reading this chapter you will understand:

- What is meant by time series data.
- What is meant by trend and seasonal terms in a time series.
- What is meant by autoregressive of order one and autocorrelation.
- The moving average method for estimating a time series model.
- How time series can be represented with difference equations.
- What is meant by a random walk and error corrections price processes.

In this chapter, we introduce the element of time as an index of a series of univariate observations. Thus, we treat observations as being obtained successively rather than simultaneously. We present a simple time series model and its components. In particular, we focus on the trend, the cyclical, and seasonal terms, as well as the error or disturbance of the model. Furthermore, we introduce the random walk and error correction models as candidates for modeling security price movements. Here the notion of innovation appears. Time series are significant in modeling price processes as well as the dynamics of economic quantities. In this chapter, we assume that trends are deterministic. In Chapter 10, we take a closer look at stochastic components of trends.

So far, we have either considered two-component variables cross-sectionally coequal, which was the case in correlation analysis, or we have considered one variable to be, at least partially, the functional result of some other quantity. The intent of this section ...

Start Free Trial

No credit card required