Exam 8: Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

When dealing with fixed effects, a de-meaned model approach is superior to the LSDV approach because the de-meaned model gives us more accurate coefficient estimates.

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Question 1

Correct Answer:

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Write down and explain the basic difference in difference equation. Point out which of the coefficients we are most interested in.

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Question 2

Correct Answer:

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We are most interested in B3, the coefficient that indicates that after taking into account the fact that the treated units may be different (B1) and that the post-treatment period may be different than the pre-treatment period for all units (B2) there are differences in the treated units in the post-treatment period
$Y _ { i t } = \beta _ { 0 } + \beta _ { 1 } \text { Treated } _ { i } + \beta _ { 2 } \text { After } _ { t } + \beta _ { 3 } \left( \text { Treated } _ { i } \times \text { After } _ { t } \right) + \epsilon _ { i t }$

Which of the following is a reason why fixed effect models can't estimate coefficients on variables that do not vary within unit?

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Question 3

Correct Answer:

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Pooled OLS models are biased when:

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Question 4

Explain why a fixed effects model is not necessary in a case where fixed effects exist but the fixed effects are not correlated with the independent variable.

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Question 5

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who in the control group after the treatment was administered is:

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Question 6

The value of the $\alpha$ _iparameter can potentially take on a different value for each unit i.

(True/False)

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Question 7

List and provide a brief explanation of three potential fixed effects that could bias a model in the analysis of the relationship between attendance at a private school and test scores.

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Question 8

Which of the following is true about fixed effects models?

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Question 9

A pooled model will produce biased coefficient estimates when factors in the error term are not correlated with the independent variable.

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Question 10

Explain why coefficients on variables that do not vary within the unit cannot be estimated in a fixed effects model.

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Question 11

Which of the following is most likely not a factor that would cause endogeneity (fixed effect is zero) when examining the relationship of bilateral trade (dependent) and trade alliances (independent).

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Question 12

Describe the issues involved with using a pooled model and given an example of why a pooled model could lead to biased results.

(Essay)

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Question 13

Having a model that controls for fixed effects even when the fixed effects are 0 ( $\alpha$ _i=0 for all units) will not cause bias.

(True/False)

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Question 14

The fixed effect ( $\alpha$ ) is the part of the unobserved error and it is different for every observation for unit i.

(True/False)

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Question 15

In order to run a two-way fixed effect model, we can use:

(Multiple Choice)

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Question 16

Which of the following factors could lead us to believe that a fixed effect model is necessary to control for endogeneity when examining the relationship between corruption (dependent) and regime type (independent) across countries over time?

(Multiple Choice)

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Question 17

The benefit of the de-meaned approach over the LSDV approach is:

(Multiple Choice)

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Question 18

In order to employ the LSDV approach in a one-way fixed effects model, we have to:

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Question 19

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who got the treatment before they received it is:

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Question 20

Showing 1 - 20 of 20

When dealing with fixed effects, a de-meaned model approach is superior to the LSDV approach because the de-meaned model gives us more accurate coefficient estimates.

Write down and explain the basic difference in difference equation. Point out which of the coefficients we are most interested in.

Which of the following is a reason why fixed effect models can't estimate coefficients on variables that do not vary within unit?

Pooled OLS models are biased when:

Explain why a fixed effects model is not necessary in a case where fixed effects exist but the fixed effects are not correlated with the independent variable.

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who in the control group after the treatment was administered is:

The value of the $\alpha$ _iparameter can potentially take on a different value for each unit i.

List and provide a brief explanation of three potential fixed effects that could bias a model in the analysis of the relationship between attendance at a private school and test scores.

Which of the following is true about fixed effects models?

A pooled model will produce biased coefficient estimates when factors in the error term are not correlated with the independent variable.

Explain why coefficients on variables that do not vary within the unit cannot be estimated in a fixed effects model.

Which of the following is most likely not a factor that would cause endogeneity (fixed effect is zero) when examining the relationship of bilateral trade (dependent) and trade alliances (independent).

Describe the issues involved with using a pooled model and given an example of why a pooled model could lead to biased results.

Having a model that controls for fixed effects even when the fixed effects are 0 ( $\alpha$ _i=0 for all units) will not cause bias.

The fixed effect ( $\alpha$ ) is the part of the unobserved error and it is different for every observation for unit i.

In order to run a two-way fixed effect model, we can use:

Which of the following factors could lead us to believe that a fixed effect model is necessary to control for endogeneity when examining the relationship between corruption (dependent) and regime type (independent) across countries over time?

The benefit of the de-meaned approach over the LSDV approach is:

In order to employ the LSDV approach in a one-way fixed effects model, we have to:

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who got the treatment before they received it is:

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying 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 8: Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

When dealing with fixed effects, a de-meaned model approach is superior to the LSDV approach because the de-meaned model gives us more accurate coefficient estimates.

Write down and explain the basic difference in difference equation. Point out which of the coefficients we are most interested in.

Which of the following is a reason why fixed effect models can't estimate coefficients on variables that do not vary within unit?

Pooled OLS models are biased when:

Explain why a fixed effects model is not necessary in a case where fixed effects exist but the fixed effects are not correlated with the independent variable.

Given Yit = B0 + B1Treatedi + B2Aftert + B3(Treatedi*Aftert) + eit, the expected value of the dependent variable for those who in the control group after the treatment was administered is:

The value of the α\alphaα i parameter can potentially take on a different value for each unit i.

List and provide a brief explanation of three potential fixed effects that could bias a model in the analysis of the relationship between attendance at a private school and test scores.

Which of the following is true about fixed effects models?

A pooled model will produce biased coefficient estimates when factors in the error term are not correlated with the independent variable.

Explain why coefficients on variables that do not vary within the unit cannot be estimated in a fixed effects model.

Which of the following is most likely not a factor that would cause endogeneity (fixed effect is zero) when examining the relationship of bilateral trade (dependent) and trade alliances (independent).

Describe the issues involved with using a pooled model and given an example of why a pooled model could lead to biased results.

Having a model that controls for fixed effects even when the fixed effects are 0 ( α\alphaα i=0 for all units) will not cause bias.

The fixed effect ( α\alphaα ) is the part of the unobserved error and it is different for every observation for unit i.

In order to run a two-way fixed effect model, we can use:

Which of the following factors could lead us to believe that a fixed effect model is necessary to control for endogeneity when examining the relationship between corruption (dependent) and regime type (independent) across countries over time?

The benefit of the de-meaned approach over the LSDV approach is:

In order to employ the LSDV approach in a one-way fixed effects model, we have to:

Given Yit = B0 + B1Treatedi + B2Aftert + B3(Treatedi*Aftert) + eit, the expected value of the dependent variable for those who got the treatment before they received it is:

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying 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

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who in the control group after the treatment was administered is:

The value of the $\alpha$ _iparameter can potentially take on a different value for each unit i.

Having a model that controls for fixed effects even when the fixed effects are 0 ( $\alpha$ _i=0 for all units) will not cause bias.

The fixed effect ( $\alpha$ ) is the part of the unobserved error and it is different for every observation for unit i.

Given Y_it= B₀+ B₁Treated_i+ B2After_t+ B3(Treated_i*After_t) + e_it, the expected value of the dependent variable for those who got the treatment before they received it is: