Question 1

Explain what multicollinearity is, and describe two ways to deal with the issue of multicollinearity:

Accepted Answer

Multicollinearity occurs when independent variables have linear relationships with each other. This can lead to issue because it decrease the precision of estimates and can make it harder to tell the effect of each variable on the dependent variable. If we have a large enough samples size with relatively small standard errors, then we don't need to do anything. Otherwise, we should make note of the multicollinearity problem and tell the reader that we may have trouble distinguishing the effects of highly multicollinear variables, but that we can at least assess whether the variables jointly have an effect or not.

Question 2

Explain goodness of fit and talk about the issue of adding irrelevant variables into the model:

Accepted Answer

Adding an additional variable into the model will increase the R² variable (or, for a pathologically bad independent variable could have no effect on it). The more explanatory power a variable has, the more adding it into the model will increase the R²value. When we include irrelevant variables into the model, we are improve the goodness of fit only a small amount. However, adding an irrelevant variable does not cause bias.

Question 3

Perfect multicollinearity means all independent variables are uncorrelated with each other.

Accepted Answer

A) True 
 B)False  False

Question 4

If the measurement error is in the independent variable, then

Accepted Answer

A)  We don't need to worry about bias, the measurement error will be reflected in the error term. 
B)  The bigger the measurement error, the bigger the variance of the error term. 
C)  We will have a case of attenuation bias, where the coefficient will be closer to 0 than it should be. 
D)  We will have a case of attenuation bias, where the coefficient is larger than it should be. 
A)  We don't need to worry about bias, the measurement error will be reflected in the error term. 
B)  The bigger the measurement error, the bigger the variance of the error term. 
C)  We will have a case of attenuation bias, where the coefficient will be closer to 0 than it should be. 
D)  We will have a case of attenuation bias, where the coefficient is larger than it should be.

Question 5

Which of the following is not a challenge for model specification?

Accepted Answer

A)  Model fishing 
B)  Finding the appropriate data set 
C)  Changes in sample size 
D)  Unclear results due to the use of multiple models 
A)  Model fishing 
B)  Finding the appropriate data set 
C)  Changes in sample size 
D)  Unclear results due to the use of multiple models

Question 6

When using an auxiliary equation to help us think though an omitted variable bias question, if $\delta$1 is equal to zero then:

Accepted Answer

A)  We have an OMV bias problem and therefore need to include the variable (if we can). 
B)  The omitted variable is not correlated with the main independent variable of interest, and we therefore do not have an omitted variable bias problem by omitting that variable. 
C)  We don't care about $\delta$1 in the auxiliary equation and instead need to focus on the size of the error term in the auxiliary equation. 
D)  Need more information to answer this question 
A)  We have an OMV bias problem and therefore need to include the variable (if we can). 
B)  The omitted variable is not correlated with the main independent variable of interest, and we therefore do not have an omitted variable bias problem by omitting that variable. 
C)  We don't care about $\delta$1 in the auxiliary equation and instead need to focus on the size of the error term in the auxiliary equation. 
D)  Need more information to answer this question

Question 7

Including irrelevant independent variables into the model:

Accepted Answer

A)  Will create bias 
B)  Will reduce the precision of our estimates. 
C)  Will have no negative or positive effect 
D)  Will lead to an increase in the error term. 
A)  Will create bias 
B)  Will reduce the precision of our estimates. 
C)  Will have no negative or positive effect 
D)  Will lead to an increase in the error term.

Question 8

Explain and contrast the consequences of having a measurement error in the independent and dependent variables.

Accepted Answer

The answer of Explain and contrast the consequences of having...

Question 9

By adding more independent variables into our OLS model, we have a greater chance of getting rid of the endogeneity that exists within the error term.

Accepted Answer

A) True 
 B)False

Question 10

If we add more independent variables into the model:

Accepted Answer

A)  The R2 value will decrease if the variables we are adding into the model should not be there. 
B)  The R2 value will increase. 
C)  The adjusted R2 value will increase. 
D)  The R2 will be biased. 
A)  The R2 value will decrease if the variables we are adding into the model should not be there. 
B)  The R2 value will increase. 
C)  The adjusted R2 value will increase. 
D)  The R2 will be biased.

Question 11

Explain the omitted variable bias problem, and show the equation(s) for determining the size of the bias.

Accepted Answer

The answer of Explain the omitted variable bias problem, and...

Question 12

Adding more control variables will always increase the R2 value.

Accepted Answer

A) True 
 B)False

Question 13

Which of the following are associated with larger omitted variable biases?

Accepted Answer

A)  The dependent variable is measured with error. 
B)  The relationship between the included and excluded variables is weak. 
C)  The relationship between the included and excluded variables is strong. 
D)  The relationship between the excluded variable and the dependent variable is weak. 
A)  The dependent variable is measured with error. 
B)  The relationship between the included and excluded variables is weak. 
C)  The relationship between the included and excluded variables is strong. 
D)  The relationship between the excluded variable and the dependent variable is weak.

Question 14

We necessarily do not have an omitted variable bias problem the omitted variable is uncorrelated with the included variable.

Accepted Answer

A) True 
 B)False

Question 15

Adding more independent variables can reduce multicollinearity.

Accepted Answer

A) True 
 B)False

Question 16

What should we do when we have multicollinearity?

Accepted Answer

A)  Ignore it no matter what. 
B)  Drop some of the independent variables. 
C)  Consider testing whether the highly collinear variables are jointly significant. 
D)  Add more independent variables in order to reduce multicollinearity. 
A)  Ignore it no matter what. 
B)  Drop some of the independent variables. 
C)  Consider testing whether the highly collinear variables are jointly significant. 
D)  Add more independent variables in order to reduce multicollinearity.

Question 17

Explain the challenges in the model specification process:

Accepted Answer

The answer of Explain the challenges in the model specification...

Question 18

Measurement error in the dependent variable causes our beta-hat estimates to be biased.

Accepted Answer

A) True 
 B)False

Question 19

Which of the following are consequences of measurement error in the dependent variable?

Accepted Answer

A)  Smaller R2 
B)  Biased coefficient estimates 
C)  The bigger the measurement error, the bigger the variance of the error term. 
D)  Both A and C 
A)  Smaller R2 
B)  Biased coefficient estimates 
C)  The bigger the measurement error, the bigger the variance of the error term. 
D)  Both A and C

Question 20

In a case where there is multicollinearity in the model

Accepted Answer

A)  Independent variables have strong linear relationships with each other 
B)  The variance of the estimates increases when we have multicollinearity. 
C)  Multicollinearity will lead to bias. 
D)  Both A and B 
E)  Both A and C 
A)  Independent variables have strong linear relationships with each other 
B)  The variance of the estimates increases when we have multicollinearity. 
C)  Multicollinearity will lead to bias. 
D)  Both A and B 
E)  Both A and C

Explain what multicollinearity is, and describe two ways to deal with the issue of multicollinearity:

Explain goodness of fit and talk about the issue of adding irrelevant variables into the model:

Perfect multicollinearity means all independent variables are uncorrelated with each other.

If the measurement error is in the independent variable, then

Which of the following is not a challenge for model specification?

When using an auxiliary equation to help us think though an omitted variable bias question, if $\delta$ ₁is equal to zero then:

Including irrelevant independent variables into the model:

Explain and contrast the consequences of having a measurement error in the independent and dependent variables.

By adding more independent variables into our OLS model, we have a greater chance of getting rid of the endogeneity that exists within the error term.

If we add more independent variables into the model:

Explain the omitted variable bias problem, and show the equation(s) for determining the size of the bias.

Adding more control variables will always increase the R² value.

Which of the following are associated with larger omitted variable biases?

We necessarily do not have an omitted variable bias problem the omitted variable is uncorrelated with the included variable.

Adding more independent variables can reduce multicollinearity.

What should we do when we have multicollinearity?

Explain the challenges in the model specification process:

Measurement error in the dependent variable causes our beta-hat estimates to be biased.

Which of the following are consequences of measurement error in the dependent variable?

In a case where there is multicollinearity in the model

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research Questions

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

Instrumental Variables: Using Exogenous Variation to Fight Endogeneity

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

Exam 5: Multivariate Ols: Where the Action Is

Explain what multicollinearity is, and describe two ways to deal with the issue of multicollinearity:

Explain goodness of fit and talk about the issue of adding irrelevant variables into the model:

Perfect multicollinearity means all independent variables are uncorrelated with each other.

If the measurement error is in the independent variable, then

Which of the following is not a challenge for model specification?

When using an auxiliary equation to help us think though an omitted variable bias question, if δ\deltaδ 1 is equal to zero then:

Including irrelevant independent variables into the model:

Explain and contrast the consequences of having a measurement error in the independent and dependent variables.

By adding more independent variables into our OLS model, we have a greater chance of getting rid of the endogeneity that exists within the error term.

If we add more independent variables into the model:

Explain the omitted variable bias problem, and show the equation(s) for determining the size of the bias.

Adding more control variables will always increase the R2 value.

Which of the following are associated with larger omitted variable biases?

We necessarily do not have an omitted variable bias problem the omitted variable is uncorrelated with the included variable.

Adding more independent variables can reduce multicollinearity.

What should we do when we have multicollinearity?

Explain the challenges in the model specification process:

Measurement error in the dependent variable causes our beta-hat estimates to be biased.

Which of the following are consequences of measurement error in the dependent variable?

In a case where there is multicollinearity in the model

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research Questions

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

Instrumental Variables: Using Exogenous Variation to Fight Endogeneity

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

When using an auxiliary equation to help us think though an omitted variable bias question, if $\delta$ ₁is equal to zero then:

Adding more control variables will always increase the R² value.