Skip to content

FRM Part I · FRM Exam Part I · Regression Diagnostics

In a multiple regression with two explanatory variables, the F-statistic for the joint significance of both slopes is very high, but neither individual t-statistic is significant at 5%. The two regressors have a sample correlation of 0.97. What is the most likely explanation and implication?

This pattern signals multicollinearity. With a 0.97 correlation between regressors, OLS cannot separate their individual effects, so standard errors are inflated and t-statistics are small, while the joint F-test stays significant. The coefficient estimates remain unbiased.

  1. AOmitted variable bias; the coefficient estimates are biased upward
  2. BSerial correlation; the F-statistic is invalid
  3. CMulticollinearity; the coefficient estimates remain unbiased but have inflated standard errorsCorrect
  4. DHeteroskedasticity; the coefficients are inconsistent

Explanation

A significant joint F-test with insignificant individual t-statistics and highly correlated regressors is the classic sign of multicollinearity. It inflates the variances of the slope estimates but does not bias them. Distractors describe other violations that do not produce this pattern.

Did you get it right without looking?

One question tells you little. A timed set on Regression Diagnostics shows your real accuracy, how long you take and where you lose marks.

Practise Regression DiagnosticsFree diagnostic mock

More Regression Diagnostics questions