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  • Print publication year: 2010
  • Online publication date: June 2012

12 - Cointegration in real estate markets


Learning outcomes

In this chapter, you will learn how to

  • highlight the problems that may occur if non-stationary data are used in their levels forms:

  • distinguish between types of non-stationarity;

  • run unit root and stationarity tests;

  • test for cointegration;

  • specify error correction models;

  • implement the Engle–Granger procedure;

  • apply the Johansen technique; and

  • forecast with cointegrated variables and error correction models.

Stationarity and unit root testing

Why are tests for non-stationarity necessary?

There are several reasons why the concept of non-stationarity is important and why it is essential that variables that are non-stationary be treated differently from those that are stationary. Two definitions of non-stationarity were presented at the start of chapter 8. For the purpose of the analysis in this chapter, a stationary series can be defined as one with a constant mean, constant variance and constant autocovariances for each given lag. The discussion in this chapter therefore relates to the concept of weak stationarity. An examination of whether a series can be viewed as stationary or not is essential for the following reasons.

• The stationarity or otherwise of a series can strongly influence its behaviour and properties. To offer one illustration, the word ‘shock’ is usually used to denote a change or an unexpected change in a variable, or perhaps simply the value of the error term during a particular time period. For a stationary series, ‘shocks’ to the system will gradually die away. That is, a shock during time t will have a smaller effect in time t + 1, a smaller effect still in time t + 2, and so on.

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