Question 1

What is omitted variable bias? Why is it a problem? How do you try to prevent it? Explain.

Accepted Answer

Omitted variable bias occurs when an ind

Question 2

The RESET test is used to

Accepted Answer

A) test for the inclusion of higher-order polynomials. 
B) test for choosing between non-nested models. 
C) test for the individual significance of coefficients. 
D) test for omitted variable bias. 
A) test for the inclusion of higher-order polynomials. 
B) test for choosing between non-nested models. 
C) test for the individual significance of coefficients. 
D) test for omitted variable bias.

Question 3

Suppose that you estimate the regression function $\text { Stock Price } _ { i } = \beta _ { 0 } + \beta _ { 1 } \text { Wealth } _ { i } + \beta _ { 2 } \text { Earnings } _ { i } + \beta _ { 3 } \text { Rainfall } _ { i } + \varepsilon _ { i }$
You might be concerned that $\hat { \beta } _ { 2 }$
May have a large standard error because

Accepted Answer

A) investor wealth is likely an irrelevant independent variable. 
B) the amount of rainfall is likely an irrelevant independent variable. 
C) investor wealth is likely measured with error. 
D) the first letter in the firm's name is likely an omitted variable. 
A) investor wealth is likely an irrelevant independent variable. 
B) the amount of rainfall is likely an irrelevant independent variable. 
C) investor wealth is likely measured with error. 
D) the first letter in the firm's name is likely an omitted variable.

Question 4

One can deal with missing data by

Accepted Answer

A) making up values for missing data. 
B) creating a dummy variable for observations with non-missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis. 
C) creating a dummy variable for observations with missing values,setting the missing values equal to 0,and including the dummy variable in the regression analysis. 
D) creating a dummy variable for observations with missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis. 
A) making up values for missing data. 
B) creating a dummy variable for observations with non-missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis. 
C) creating a dummy variable for observations with missing values,setting the missing values equal to 0,and including the dummy variable in the regression analysis. 
D) creating a dummy variable for observations with missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis.

Question 5

Omitted variable bias occurs when one does not include

Accepted Answer

A) an independent variable that is correlated with the dependent variable only. 
B) an independent variable that is correlated with the dependent variable and an included independent variable. 
C) an independent variable that is correlated with an included independent variable only. 
D) a dependent variable that is correlated with an included independent variable. 
A) an independent variable that is correlated with the dependent variable only. 
B) an independent variable that is correlated with the dependent variable and an included independent variable. 
C) an independent variable that is correlated with an included independent variable only. 
D) a dependent variable that is correlated with an included independent variable.

Question 6

One can deal with missing data by

Accepted Answer

A) dropping observations with missing data if the missing data is random and therefore will not affect the resulting coefficient estimates. 
B) making up values for missing data. 
C) ignoring the potential impact of the missing data in the analysis. 
D) running a regression with the number 6 put in lieu of the missing data. 
A) dropping observations with missing data if the missing data is random and therefore will not affect the resulting coefficient estimates. 
B) making up values for missing data. 
C) ignoring the potential impact of the missing data in the analysis. 
D) running a regression with the number 6 put in lieu of the missing data.

Question 7

What is the "eye test"? Why is it important to employ it every time you estimates a sample regression function? Explain.

Accepted Answer

The "eye test" refers to examining your

Question 8

Potential inclusion of irrelevant variables is best dealt with by

Accepted Answer

A) carefully considering economic theory. 
B) performing tests of individual significance. 
C) performing tests of joint significance. 
D) including the fewest possible independent variables. 
A) carefully considering economic theory. 
B) performing tests of individual significance. 
C) performing tests of joint significance. 
D) including the fewest possible independent variables.

Question 9

Omitted variable bias is a potential problem because it

Accepted Answer

A) prevents accurately estimating true marginal effects. 
B) results in estimated standard errors that are too large. 
C) results in inefficient parameter estimates. 
D) might highlight spurious correlations. 
A) prevents accurately estimating true marginal effects. 
B) results in estimated standard errors that are too large. 
C) results in inefficient parameter estimates. 
D) might highlight spurious correlations.

Question 10

Suppose you estimate restaurant sales as a function of the quality of SERVICE,PRICE,and consumer INCOME and you get the following results (p-values in parentheses) $\begin{array} { lllll } 
\text { Sales } _ { i } = & 4,567.29 + &1,245.77 \text { Service } _ { i } + &849.36 \text { Price } _ { i } +& 72.81 \text { Income } _ { i } \
&( 0.003 ) & ( 0.096 ) & ( 0.103 ) & ( 0.047 )
\end{array}$
Given our usual language,we would conclude that

Accepted Answer

A) SERVICE,PRICE,and INCOME are all statistically significant. 
B) INCOME and SERVICE are statistically significant;PRICE is marginally significant. 
C) INCOME is statistically significant;SERVICE and PRICE are marginally significant. 
D) INCOME is statistically significant;SERVICE are marginally significant;PRICE is statistically insignificant. 
A) SERVICE,PRICE,and INCOME are all statistically significant. 
B) INCOME and SERVICE are statistically significant;PRICE is marginally significant. 
C) INCOME is statistically significant;SERVICE and PRICE are marginally significant. 
D) INCOME is statistically significant;SERVICE are marginally significant;PRICE is statistically insignificant.

Question 11

All of the following are potential problems associated with missing data except

Accepted Answer

A) dropping observations with missing data will bias the estimated coefficients if the missing data is due to selection bias. 
B) the number of observations goes down. 
C) dropping the independent variables that have missing data will typically bias the estimated coefficients. 
D) missing data always increases the estimated standard errors. 
A) dropping observations with missing data will bias the estimated coefficients if the missing data is due to selection bias. 
B) the number of observations goes down. 
C) dropping the independent variables that have missing data will typically bias the estimated coefficients. 
D) missing data always increases the estimated standard errors.

What is omitted variable bias? Why is it a problem? How do you try to prevent it? Explain.

The RESET test is used to

One can deal with missing data by

Omitted variable bias occurs when one does not include

One can deal with missing data by

What is the "eye test"? Why is it important to employ it every time you estimates a sample regression function? Explain.

Potential inclusion of irrelevant variables is best dealt with by

Omitted variable bias is a potential problem because it

Suppose you estimate restaurant sales as a function of the quality of SERVICE,PRICE,and consumer INCOME and you get the following results (p-values in parentheses) = 4,567.29+ 1,245.77+ 849.36+ 72.81 (0.003) (0.096) (0.103) (0.047) Given our usual language,we would conclude that

All of the following are potential problems associated with missing data except

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Auto-Correlation

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters

Exam 8: Model Selection in Multiple Linear Regression Analysis

What is omitted variable bias? Why is it a problem? How do you try to prevent it? Explain.

The RESET test is used to

One can deal with missing data by

Omitted variable bias occurs when one does not include

One can deal with missing data by

What is the "eye test"? Why is it important to employ it every time you estimates a sample regression function? Explain.

Potential inclusion of irrelevant variables is best dealt with by

Omitted variable bias is a potential problem because it

Suppose you estimate restaurant sales as a function of the quality of SERVICE,PRICE,and consumer INCOME and you get the following results (p-values in parentheses) = 4,567.29+ 1,245.77+ 849.36+ 72.81 (0.003) (0.096) (0.103) (0.047) Given our usual language,we would conclude that

All of the following are potential problems associated with missing data except

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Auto-Correlation

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters