How are the Johansen tests related to cointegration?
The Johansen tests are likelihood-ratio tests. There are two tests: 1. the maximum eigenvalue test, and 2. the trace test. For both test statistics, the initial Johansen test is a test of the null hypothesis of no coin- tegration against the alternative of cointegration. The tests dier in terms of the alternative
Which is the two step test for cointegration?
The Engle-Granger Two-Step method starts by creating residuals based on the static regression and then testing the residuals for the presence of unit roots. It uses the Augmented Dickey-Fuller Test (ADF) or other tests to test for stationarity units in time series.
When did Robert Engle invent the cointegration test?
1 Cointegration is a technique used to find a possible correlation between time series processes in the long term. 2 Nobel laureates Robert Engle and Clive Granger introduced the concept of cointegration in 1987. 3 The most popular cointegration tests include Engle-Granger, the Johansen Test, and the Phillips-Ouliaris test.
When was the cointegration test first used in economics?
A cointegration test is used to establish if there is a correlation between several time series in the long term. The concept was first introduced by Nobel laureates Robert Engle and Clive Granger, in 1987, after British economist Paul Newbold and Granger published the spurious regression concept.
What kind of test is the Johansen test?
The Johansen tests are likelihood-ratio tests. There are two tests: 1. the maximum eigenvalue test, and 2. the trace test. For both test statistics, the initial Johansen test is a test of the null hypothesis of no coin- tegration against the alternative of cointegration.
How does the Engle Granger cointegration test work?
The Engle-Granger cointegration test considers the case that there is a single cointegrating vector. The test follows the very simple intuition that if variables are cointegrated, then the residual of the cointegrating regression should be stationary. Forming the cointegrating residual
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.