Consider the multiple regression model with two regressors X₁ and X₂, where both variables are determinants of the dependent variable. You first regress Y on X₁ only and find no relationship. However when regressing Y on X₁ and X₂, the slope coefficient $\widehat { \beta 1 }$ changes by a large amount. This suggests that your first regression suffers from A)heteroskedasticity B)perfect multicollinearity C)omitted variable bias D)dummy variable trap

Question

Quizplus · Accepted Answer

When the inclusion of an additional variable in a regression model significantly changes the coefficient of an existing variable, it suggests that the initial model omitted a relevant variable that has a correlation with both the dependent variable and the included independent variable. This scenario is indicative of omitted variable bias.

Consider the Multiple Regression Model with Two Regressors X1 and X2

Consider the Multiple Regression Model with Two Regressors X₁ and X₂