Multicollinearity

Strong correlation among the explanatory variables in a regression. It inflates standard errors and makes individual coefficients hard to interpret.

Where you will meet it

Multicollinearity comes up in 5 learning outcome notes across the FRM syllabus. A few of them:

• Regression with Multiple Explanatory Variables
  FRM Part I · Quantitative analysis

  “There must be no exact linear relationship among the explanatory variables, so no perfect multicollinearity.”

• Regression with Multiple Explanatory Variables
  FRM Part I · Quantitative analysis

  “Individual t-statistics can all be insignificant while the joint F-test rejects, a typical symptom of multicollinearity.”

• Regression Diagnostics
  FRM Part I · Quantitative analysis

  “Multicollinearity means two or more regressors are highly, but not perfectly, correlated.”

• Non-Stationary Time Series
  FRM Part I · Quantitative analysis

  “Including all s makes them add up to the intercept column and produces perfect multicollinearity, the dummy variable trap.”

See also

Correlation · Regression

All terms beginning with M · FRM glossary

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