- 13.06.2019

The asymptotic distribution of the LR test statistic for cointegration does not have the usual distribution and depends on the assumptions made with respect to deterministic trends. Therefore, in order to carry out the test, you need to make an assumption regarding the trend underlying your data. Cases 2 and 4 do not have the same set of deterministic terms in the two columns. For these two cases, some of the deterministic term is restricted to belong only in the cointegrating relation.

For cases 3 and 5, the deterministic terms are common in the two columns and the decomposition of the deterministic effects inside and outside the cointegrating space is not uniquely identified; see the technical discussion below. In practice, cases 1 and 5 are rarely used. You should use case 1 only if you know that all series have zero mean. Case 5 may provide a good fit in-sample but will produce implausible forecasts out-of-sample.

As a rough guide, use case 2 if none of the series appear to have a trend. For trending series, use case 3 if you believe all trends are stochastic; if you believe some of the series are trend stationary, use case 4. If you are not certain which trend assumption to use, you may choose the Summary of all 5 trend assumptions option case 6 to help you determine the choice of the trend assumption. This option indicates the number of cointegrating relations under each of the 5 trend assumptions, and you will be able to assess the sensitivity of the results to the trend assumption.

We may summarize the five deterministic trend cases considered by Johansen , p. The level data have no deterministic trends and the cointegrating equations do not have intercepts: 2. The level data have no deterministic trends and the cointegrating equations have intercepts: 3.

The level data have linear trends but the cointegrating equations have only intercepts: 4. The level data and the cointegrating equations have linear trends: 5.

What if we have more than one? The test may be less powerful than the trace test for the same values. A special case for using the maximum eigenvalue test is when , where rejecting the null hypothesis implies the existence of m possible linear combinations.

This is impossible, unless all input time series variables are stationary to start with. In NumXL, the Johansen test combines these two test forms to examine the cointegration assumption: Trace Test for Maximum Eigenvalue Test for To establish the existence of cointegration in a set of time series variables, we wish to reject the trace test null hypothesis and not reject the null hypothesis of the maximum eigenvalue test.

Step 1 Organized your input time series data as adjacent columns. Each column represents one variable and each row corresponds to an observation. Step 2: Locate the cointegration test icon in the NumXL menu or toolbar and click on it. Step 3: Using the cointegration wizard, select your input variables. The selection may include column labels. In our tutorial, we want to include all of them, so we can leave it blank.

Step 4: Optional Initially, all Johansen tests are selected and a maximum lag i order is calculated from the input data, but you can override any of those options as you see fit. Step 5: Optional If your input data does not have any missing values, you may skip this step. By default, the cointegration wizard will trigger an error if any of the variables has a missing value. This is acceptable for this tutorial. We examined this question under different assumption for the input variable, and they all passed.

Thus, we can conclude that the variables are cointegrated. Next, under the maximum eigenvalue test, we want to be sure that the number of linear combinations does not equal the number of input variables. Because if they do, the input variables are stationary to start with, and cointegration is not relevant. Again, we carry on the test under different assumptions for the input variables. In this example, they all failed the test aside from one scenario, which passed marginally.

In conclusion, we would state that the input variables are cointegrated. Now what?

We may summarize the five deterministic trend cases considered by Johansen , p. For cases 3 and 5, the deterministic terms are common in the two columns and the decomposition of the deterministic effects inside and outside the cointegrating space is not uniquely identified; see the technical discussion below. All time-series variables are stationary to start with. All time-series variables are stationary to start with constant and linear trend. What if we have more than one. With such factual and visible evidence enforcing existing bias. You may even find some articles that could be.- Case study and their solutions;
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Similarly, the cointegrating equations may have intercepts and deterministic trends. Therefore, in order to carry out the test, you need to make an assumption regarding the trend underlying your data. For cases 3 and 5, the deterministic terms are common in the two columns and the decomposition of the deterministic effects inside and outside the cointegrating space is not uniquely identified; see the technical discussion below. More specifically, we identify the part inside the error correction term by regressing the cointegrating relations on a constant and linear trend.

**Meztilkree**

More specifically, we identify the part inside the error correction term by regressing the cointegrating relations on a constant and linear trend.

**Jumuro**

As a rough guide, use case 2 if none of the series appear to have a trend. As explained below, the elements of are known as the adjustment parameters in the VEC model.

**JoJor**

In practice, cases 1 and 5 are rarely used. You may wish first to apply unit root tests to each series in the VAR. The difference, however, is an alternate hypothesis: So, starting with and rejecting the null hypothesis implies that there is only one possible combination of the non-stationary variables to yield a stationary process. To handle this problem, Johansen , page 84 suggests using centered orthogonalized seasonal dummy variables, which shift the mean without contributing to the trend.

**Mazukazahn**

If you are not certain which trend assumption to use, you may choose the Summary of all 5 trend assumptions option case 6 to help you determine the choice of the trend assumption. EViews uses a different identification method so that the error correction term has a sample mean of zero. If this the case, then we conclude there is at least one cointegration relationship.

**Nishicage**

As a rough guide, use case 2 if none of the series appear to have a trend. Next, under the maximum eigenvalue test, we want to be sure that the number of linear combinations does not equal the number of input variables. The selection may include column labels. The level data and the cointegrating equations have linear trends: 5. One important test for cointegration that is invariant to the ordering of variables is the full-information maximum likelihood test of Johansen aka Johansen test.

**Taurg**

This option indicates the number of cointegrating relations under each of the 5 trend assumptions, and you will be able to assess the sensitivity of the results to the trend assumption. The dialog will differ slightly depending on whether you are using a group or an estimated Var object to perform your test. Deterministic Trend Specification Your series may have nonzero means and deterministic trends as well as stochastic trends. You may wish first to apply unit root tests to each series in the VAR. To handle this problem, Johansen , page 84 suggests using centered orthogonalized seasonal dummy variables, which shift the mean without contributing to the trend.

**Zolosida**

It turns out that it does matter.

**Grojora**

In general, given a set of non-stationary of type time series variables , there exists a linear combination consisting of all variables with a vector , such that:.

**Arashigal**

In conclusion, we would state that the input variables are cointegrated.

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