Running auxillary regressions where each explanatory variable is estimated as a function of eth remaining explanatory variables can help detect
A)omitted relevant variables
B)irrelevant variables included
C)collinearity
D)heteroskedasiticity

Question

Quizplus · Accepted Answer

Running auxiliary regressions where each explanatory variable is regressed on the remaining explanatory variables helps in detecting collinearity (or multicollinearity), which occurs when two or more explanatory variables in a multiple regression model are highly linearly related.

Running Auxillary Regressions Where Each Explanatory Variable Is Estimated as a Function