Prueba Cointegración (Johansen)

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Hello and welcome to the NumXL cointegration test tutorial.

In time series analysis, we often encounter situations where we wish to model one non-stationary time series as a linear combination of other non-stationary time series. Ignoring the cointegration aspect in time series variables may lead to a spurious regression problem, and yield the model unstable.

In this tutorial, we will demosntrate the steps to examine the cointegration issue in the time series data set. For our example, we are using the monthly employment data in 8 states in mining from January 1982 to May 1996.

First, Organized your input time series data as adjacent columns. Each column represents one variable and each row corresponds to an observation.

Locate the cointegration test icon in the NumXL menu or toolbar and click on it.

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Using the cointegration wizard, select your input variables. The selection may include column labels.

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 Note, the Mask field is used to exclude variable/columns from the analysis without changing your input data in the worksheet. In our tutorial, we want to include all of them, so we can leave it blank.

Note that the "Options" and "Missing Values" tabs are enabled.

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Initially, all Johansen tests are selected and a maximum lag order is calculated from the input data, but you can override any of those options as you see fit.

Let’s leave it unchanged

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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.

Click the "OK" button.

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The Johansen test generates two tables.

(1) Under the trace test, we asked whether there’s at least one possible linear combination for the input variables to yield a stationary process. We examined this question under different assumptions for the input variables, and they all passed. Thus, we can conclude that the variables are cointegrated

(2) 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. Why? 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.

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That is it for now, thanks for watching!