Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable Y. You first regress Y on X1 and find no relationship. However, while regressing Y on X1 and X2 the slope coefficient of the variable X1 changes by a large amount. This suggests that your first regression suffers from:

A. Perfect multicollinearity

B. Dummy variable trap

C. Omitted variable bias

D. Heteroskedasticity

Question

Skippy

Mathematics

29 January, 07:52

Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable Y. You first regress Y on X1 and find no relationship. However, while regressing Y on X1 and X2 the slope coefficient of the variable X1 changes by a large amount. This suggests that your first regression suffers from:

A. Perfect multicollinearity

B. Dummy variable trap

C. Omitted variable bias

D. Heteroskedasticity

+1

Answers (1)

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Dillan Pham · Answer 1

C. Omitted variable bias

Step-by-step explanation:

In mathematics and statistics, omitted-variable bias (OVB) happens if one or more important variables is left out by a statistical model.

The bias results in the equation being related to the expected effects of the included variables by the influence of the excluded variables.