VAR order estimation: If the automatic option is selected for the VAR order, this table displays the four criteria values for the VAR order estimation. It is not limited to two time series and allows you to test the existence of multiple cointegrating relationships. This approach, implemented in XLSTAT, is based on Vector Autoregressive (VAR) models. One of the most interesting approaches for testing for cointegration within a group of time series is the maximum likelihood methodology proposed by Johansen (1988, 1991). Those stationary combinations are called cointegrating equations. In other words, there exists one or more linear combination of those I(1) time series (or integrated of order 1, see unit root test) that is stationary (or I(0)). It identifies a situation where two or more non stationary time series are bound together in such a way that they cannot deviate from some equilibrium in the long term. The term of cointegration was first introduced by Engle and Granger (1987) after the work of Granger and Newbold (1974) on spurious regression. In finance, such relationships are expected for instance between the prices of the same asset on different market places. Examples of such relationships in economics include money with income, prices and interest rates or exchange rate with foreign and domestic prices. We say that those variables are cointegrated. Although those variables can derive from each other on a short term basis, the economic forces at work should restore the original equilibrium between them on the long run. What are cointegration tests?Įconomic theory often suggests long-term relationship between two or more economic variables. Use this module to perform VAR-based cointegration tests on a group of two or more I(1) time series using the approach proposed by Johansen (1991, 1995).
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