Suppose that you estimate the sample regression function $$ swidehat { alar}y = \hat { \beta } _ { 0 } + \hat { \beta } _ { 1 } \text { Experience } _ { i } + \hat { \beta } _ { 2 } \text { Male } _ { i } + \hat { \beta } _ { 3 } \text { Married } _ { i }$$
You might be concerned that $\hat { \beta } _ { 1 }$
Is a biased estimate of the marginal effect of experience on salary because
A)a person's sex is likely an irrelevant independent variable.
B)marital status is likely an irrelevant independent variable.
C)education is likely an omitted independent variable.
D)the identity of the last NCAA basketball champion is likely an omitted independent variable.

Question

Suppose that you estimate the sample regression function $$ swidehat { alar}y  = \hat { \beta } _ { 0 } + \hat { \beta } _ { 1 } \text { Experience } _ { i } + \hat { \beta } _ { 2 } \text { Male } _ { i } + \hat { \beta } _ { 3 } \text { Married } _ { i }$$
You might be concerned that $\hat { \beta } _ { 1 }$
Is a biased estimate of the marginal effect of experience on salary because
A)a person's sex is likely an irrelevant independent variable.
B)marital status is likely an irrelevant independent variable.
C)education is likely an omitted independent variable.
D)the identity of the last NCAA basketball champion is likely an omitted independent variable.

Quizplus · Accepted Answer

If education is a relevant independent variable, then omitting it from the regression will result in biased estimates of the coefficients of the other variables, including $\hat { \beta } _ { 1 }$. The inclusion of irrelevant variables such as Male and Married may affect the precision of the estimates, but it will not bias the coefficients. The identity of the last NCAA basketball champion is not a relevant independent variable in determining salary, so its omission would not result in biased estimates of the coefficients.

Suppose That You Estimate the Sample Regression Function You Might Be Concerned That