Exam 9: Instrumental Variables: Using Exogenous Variation to Fight Endogen

Given the model Crime_it=B₀+B₁Police_i,t+e_it, Which one of the following potential instrumental variables do you think would not satisfy the inclusion condition?

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(Multiple Choice)

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Question 1

Correct Answer:

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Explain why attrition in randomized experiments can cause a problem, and explain one method of dealing with the problem of attrition.

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(Essay)

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Question 2

Correct Answer:

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Attrition occurs when individuals drop out from the experiment, and we therefore do not have an observation for those individuals If attrition is random in both control and treatment groups, then it will not cause bias (although it will cause estimates to be less precise due to the decrease in sample size However, non-random attrition is a problem since it can lead to endogeneity, since the reason for attrition can be correlated with factors in the error term Attrition can be addressed by using either a multivariate OLS b using a trimmed data set or c using a selection model

Which of the following is a justification for overidentification tests?

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Question 3

Correct Answer:

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An instrumental variable explains both the endogenous independent variable of interest and the dependent variable.

(True/False)

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Question 4

The variance of coefficient estimates in 2SLS is generally smaller than the variance of coefficient estimates in OLS.

(True/False)

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Question 5

Describe the process of blocking in a randomized experiment.

(Essay)

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Question 6

The variance of 2SLS Bj will be lower if:

(Multiple Choice)

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Question 7

The inclusion condition states that the instrument used must be a statistically significant determinant of Y.

(True/False)

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Question 8

A weak instrument (an instrument that does a poor job of explaining the endogenous variable) will always produce better results than OLS.

(True/False)

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Question 9

Please explain what an overidentification test is.

(Essay)

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Question 10

We cannot test if an instrument satisfies the exclusion condition statistically.

(True/False)

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Question 11

Please compare the benefits of using 2SLS and ITT approaches to deal with non-compliance.

(Essay)

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Question 12

Which one of the following are factors that influence the variance of 2SLS Bj estimates?

(Multiple Choice)

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Question 13

If we use a quasi instrument for to estimate a 2SLS, than the probability limit for the 2SLS estimate of B1^ is: (Note s=standard deviation)

(Multiple Choice)

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Question 14

Which of the following best characterizes the conditions for a good instrument?

(Multiple Choice)

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Question 15

Please explain how non-compliance could lead to a problem of endogeneity, and explain how ITT can be used to deal with the problem.

(Essay)

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Question 16

Given the model Crime_it=B₀+B₁Police_i,t+e_it Which one of the following variables do you think would satisfy the exclusion condition?

(Multiple Choice)

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Question 17

To conduct an overidentification test we run 2SLS models separately for each instrumental variable.

(True/False)

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Question 18

Give the probability limit equations for OLS and 2SLS, and explain the scenario where 2SLS should be used instead of OLS when we have an imperfect instrument (either weak or quasi)

(Essay)

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Question 19

In order to statistically verify that an instrument satisfies the exclusion condition, we can:

(Multiple Choice)

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Question 20

Showing 1 - 20 of 26

Given the model Crime_it=B₀+B₁Police_i,t+e_it, Which one of the following potential instrumental variables do you think would not satisfy the inclusion condition?

Explain why attrition in randomized experiments can cause a problem, and explain one method of dealing with the problem of attrition.

Which of the following is a justification for overidentification tests?

An instrumental variable explains both the endogenous independent variable of interest and the dependent variable.

The variance of coefficient estimates in 2SLS is generally smaller than the variance of coefficient estimates in OLS.

Describe the process of blocking in a randomized experiment.

The variance of 2SLS Bj will be lower if:

The inclusion condition states that the instrument used must be a statistically significant determinant of Y.

A weak instrument (an instrument that does a poor job of explaining the endogenous variable) will always produce better results than OLS.

Please explain what an overidentification test is.

We cannot test if an instrument satisfies the exclusion condition statistically.

Please compare the benefits of using 2SLS and ITT approaches to deal with non-compliance.

Which one of the following are factors that influence the variance of 2SLS Bj estimates?

If we use a quasi instrument for to estimate a 2SLS, than the probability limit for the 2SLS estimate of B1^ is: (Note s=standard deviation)

Which of the following best characterizes the conditions for a good instrument?

Please explain how non-compliance could lead to a problem of endogeneity, and explain how ITT can be used to deal with the problem.

Given the model Crime_it=B₀+B₁Police_i,t+e_it Which one of the following variables do you think would satisfy the exclusion condition?

To conduct an overidentification test we run 2SLS models separately for each instrumental variable.

Give the probability limit equations for OLS and 2SLS, and explain the scenario where 2SLS should be used instead of OLS when we have an imperfect instrument (either weak or quasi)

In order to statistically verify that an instrument satisfies the exclusion condition, we can:

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

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 9: Instrumental Variables: Using Exogenous Variation to Fight Endogen

Given the model Crimeit=B0+B1Policei,t+eit, Which one of the following potential instrumental variables do you think would not satisfy the inclusion condition?

Explain why attrition in randomized experiments can cause a problem, and explain one method of dealing with the problem of attrition.

Which of the following is a justification for overidentification tests?

An instrumental variable explains both the endogenous independent variable of interest and the dependent variable.

The variance of coefficient estimates in 2SLS is generally smaller than the variance of coefficient estimates in OLS.

Describe the process of blocking in a randomized experiment.

The variance of 2SLS Bj will be lower if:

The inclusion condition states that the instrument used must be a statistically significant determinant of Y.

A weak instrument (an instrument that does a poor job of explaining the endogenous variable) will always produce better results than OLS.

Please explain what an overidentification test is.

We cannot test if an instrument satisfies the exclusion condition statistically.

Please compare the benefits of using 2SLS and ITT approaches to deal with non-compliance.

Which one of the following are factors that influence the variance of 2SLS Bj estimates?

If we use a quasi instrument for to estimate a 2SLS, than the probability limit for the 2SLS estimate of B1^ is: (Note s=standard deviation)

Which of the following best characterizes the conditions for a good instrument?

Please explain how non-compliance could lead to a problem of endogeneity, and explain how ITT can be used to deal with the problem.

Given the model Crimeit=B0+B1Policei,t+eit Which one of the following variables do you think would satisfy the exclusion condition?

To conduct an overidentification test we run 2SLS models separately for each instrumental variable.

Give the probability limit equations for OLS and 2SLS, and explain the scenario where 2SLS should be used instead of OLS when we have an imperfect instrument (either weak or quasi)

In order to statistically verify that an instrument satisfies the exclusion condition, we can:

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

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

Given the model Crime_it=B₀+B₁Police_i,t+e_it, Which one of the following potential instrumental variables do you think would not satisfy the inclusion condition?

Given the model Crime_it=B₀+B₁Police_i,t+e_it Which one of the following variables do you think would satisfy the exclusion condition?