Interpreting Our Cointegration Results The Engle-Granger test statistic for cointegration reduces to an ADF unit root test of the residuals of the cointegration regression: If the residuals contain a unit root, then there is no cointegration. The null hypothesis of the ADF test is that the residuals have a unit root.

How do you interpret cointegration results?

Interpreting Our Cointegration Results The Engle-Granger test statistic for cointegration reduces to an ADF unit root test of the residuals of the cointegration regression: If the residuals contain a unit root, then there is no cointegration. The null hypothesis of the ADF test is that the residuals have a unit root.

How do I check my cointegration?

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.

What is cointegration used for?

Cointegration is a statistical method used to test the correlation between two or more non-stationary time series in the long-run or for a specified time period. The method helps in identifying long-run parameters or equilibrium for two or more sets of variables.

How do you read Johansen cointegration?

Interpreting Johansen Cointegration Test Results

1. The EViews output releases two statistics, Trace Statistic and Max-Eigen Statistic.
2. Rejection criteria is at 0.05 level.
3. Rejection of the null hypothesis is indicated by an asterisk sign (*)
4. Reject the null hypothesis if the probability value is less than or equal to 0.05.

What is cointegration testing?

Cointegration tests identify scenarios where two or more non-stationary time series are integrated together in a way that they cannot deviate from equilibrium in the long term. The tests are used to identify the degree of sensitivity of two variables to the same average price over a specified period of time.

What does cointegration mean?

Does cointegration imply correlation?

The correlation is used to check for the linear relationship (or linear interdependence) between two variables while co-integration is used to check for the existence of a long-run relationship between two or more variables.

Why is cointegration test important?

What is the purpose of cointegration?

What tests does EViews provide for system cointegration?

In the single equation setting, EViews provides views that perform Engle and Granger (1987) and Phillips and Ouliaris (1990) residual-based tests, Hansen’s instability test (Hansen 1992b), and Park’s added variables test (Park 1992). System cointegration testing using Johansen’s methodology is described in “Johansen Cointegration Test”.

What are the results of the cointegration test?

After running our cointegration test we obtain the following results: Our test statistic of -2.105 is larger than the critical values at the 1%, 5%, and 10% levels. We cannot reject the null hypothesis of no cointegration.

Are the variables cointegrated?

We have stronger evidence that the variables are cointegrated. After running our cointegration test we obtain the following results: Our test statistic of -2.105 is larger than the critical values at the 1%, 5%, and 10% levels. We cannot reject the null hypothesis of no cointegration.

What should one expect under the alternative hypothesis of no cointegration?

He notes that under the alternative hypothesis of no cointegration, one should expect to see evidence of parameter instability. He proposes (among others) use of the test statistic, which arises from the theory of Lagrange Multiplier tests for parameter instability, to evaluate the stability of the parameters.