Contents
How does the Johansen test work?
Johansen’s test is a way to determine if three or more time series are cointegrated. More specifically, it assesses the validity of a cointegrating relationship, using a maximum likelihood estimates (MLE) approach.
When should we use Johansen test?
The Johansen test is used to test cointegrating relationships between several non-stationary time series data. Compared to the Engle-Granger test, the Johansen test allows for more than one cointegrating relationship.
How is the Johansen test for cointegrating time series analysis?
The logic is that all three should in some part be affected by stochastic trends in commodities and thus may form a cointegrating relationship. In Ernie’s work he carried out the Johansen procedure using MatLab and was able to reject the hypothesis of r ≤ 2 at the 5% level.
Which is the largest eigenvalue in the Johansen test?
The answer is to make use of the eigenvector components of the eigenvector associated with the largest eigenvalue. We previously mentioned that the largest eigenvalue is approximately 0.3389. It corresponds to the vector given under the column p.l2, and is approximately equal to ( 1.000000, 1.791324, − 1.717271).
Why is the Johansen test less statistical power than CADF?
In the Johansen test the linear combination values are estimated as part of the test, which implies that there is less statistical power associated with the test when compared to CADF. It is possible to run into situations where there is insufficient evidence to reject the null hypothesis of no cointegration despite the CADF suggesting otherwise.
How to test for the presence of cointegration?
For each of these three tests we have not only the statistic itself (given under the test column) but also the critical values at certain levels of confidence: 10%, 5% and 1% respectively. The first hypothesis, r = 0, tests for the presence of cointegration.