O'Reilly logo

Applied Econometric Times Series, 3rd Edition by Walter Enders

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

CHAPTER 6

image

COINTEGRATION AND ERROR-CORRECTION MODELS

This chapter explores an exciting development in econometrics: the estimation of a structural equation or a VAR containing nonstationary variables. In univariate models, we have seen that a stochastic trend can be removed by differencing. The resulting stationary series can be estimated using univariate Box–Jenkins techniques. At one time, the conventional wisdom was to generalize this idea and difference all nonstationary variables used in a regression analysis. However, the appropriate way to treat nonstationary variables is not so straightforward in a multivariate context. It is quite possible for there to be a linear combination of integrated variables that is stationary; such variables are said to be cointegrated. Many economic models entail such cointegrating relationships. This chapter has three aims:

  1. Introduce the basic concept of cointegration and show that it applies in a variety of economic models. Any equilibrium relationship among a set of nonstationary variables implies that their stochastic trends must be linked. After all, the equilibrium relationship means that the variables cannot move independently of each other. This linkage among the stochastic trends necessitates that the variables be cointegrated.
  2. Consider the dynamic paths of cointegrated variables. Since the trends of cointegrated variables are linked, ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required