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Assess Collinearity Among Multiple Series Using Econometric Modeler App

This example shows how to assess the strengths and sources of collinearity among multiple series by using Belsley collinearity diagnostics in the Econometric Modeler app. The data set, stored in Data_Canada, contains annual Canadian inflation and interest rates from 1954 through 1994.

At the command line, load the Data_Canada.mat data set.

load Data_Canada

At the command line, open the Econometric Modeler app.


Alternatively, open the app from the apps gallery (see Econometric Modeler).

Import DataTimeTable into the app:

  1. On the Econometric Modeler tab, in the Import section, click the Import button .

  2. In the Import Data dialog box, in the Import? column, select the check box for the DataTimeTable variable.

  3. Click Import.

The Canadian interest and inflation rate variables appear in the Time Series pane, and a time series plot of all the series appears in the Time Series Plot(INF_C) figure window.

This time series plot shows the paths of variables INF_C, INF_G, INT_L, INT_M, and INT_S and the x axis is labelled as index

Perform Belsley collinearity diagnostics on all series. On the Econometric Modeler tab, in the Tests section, click New Test > Belsley Collinearity Diagnostics.

The Collinearity(INF_C) document appear with the following results:

  • A table of singular values, corresponding condition indices, and corresponding variable variance-decomposition proportions

  • A plot of the variable variance-decomposition proportions corresponding to the condition index that is above the threshold, and a horizontal line indicating the variance-decomposition threshold

A screen shot of the Collinearity tab for Belsley Collinearity Diagnostics of the Data_Canada data set. A table shows columns entitled singular values, condition indices, INF_C, INF_G, INT_L, INT_M, and INT_S. There are 5 rows of parameter values, with the final row highlighted in yellow. The graph below the table is entitled High Index Variance Decompositions and the Y axis shows Variance-Decomposition Proportions of the variables listed on the x-axis; INF_C, INF_G, INT_L, INT_M, and INT_S.

The interest rates have variance-decomposition proportions exceeding the default tolerance, 0.5, indicated by red markers in the plot. This result suggests that the interest rates exhibit multicollinearity. If you use the three interest rates as predictors in a linear regression model, then the predictor data matrix can be ill conditioned.

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