Question 1

Explain why it is important to test for autocorrelation when using a model with a lagged dependent variable and no fixed effects.

Accepted Answer

The lagged dependent variable is a function of the lagged error term If the errors are autocorrelated, then the current error term is correlated with the error term from the previous period, which means that the current error term is correlated with the lagged dependent variable We can estimate an OLS model with a lagged dependent variable and then use standard tools to assess if the errors are correlated If they are not correlated, we can use the OLS results Otherwise, we need to ρ-transform the data

Question 2

Suppose that we have the following results from a model with lagged dependent variable
Yit = 0.9Yi,t-1 + 2X1it
Which of the following is most accurate?

Accepted Answer

A)  The short-term effect of a one unit change in X will be a 2.9 unit change in Y. 
B)  The long-term effect of a one unit change in X will be a 20 unit change in Y. 
C)  The long-term effect of a one unit change in X will be a 4 unit change in Y. 
D)  Y will keep getting smaller and smaller over time. 
A)  The short-term effect of a one unit change in X will be a 2.9 unit change in Y. 
B)  The long-term effect of a one unit change in X will be a 20 unit change in Y. 
C)  The long-term effect of a one unit change in X will be a 4 unit change in Y. 
D)  Y will keep getting smaller and smaller over time. B

Question 3

Autocorrelation causes bias in a model without fixed effects.

Accepted Answer

A) True 
 B)False  False

Question 4

OLS coefficients are biased when there is a lagged dependent variable and fixed effects in the same model. Which of the following statements is most accurate about using OLS when there is a lagged dependent variable and fixed effects in the same model?

Accepted Answer

A)  We should never use OLS in this situation. 
B)  Instrumental variables are easier to use OLS in this situation. 
C)  OLS can produce coefficient estimates that are biased, but still on average closer to the true values in this situation. 
D)  OLS performs worse in this situation when we have many observations for each unit. 
A)  We should never use OLS in this situation. 
B)  Instrumental variables are easier to use OLS in this situation. 
C)  OLS can produce coefficient estimates that are biased, but still on average closer to the true values in this situation. 
D)  OLS performs worse in this situation when we have many observations for each unit.

Question 5

What factor is associated with lower bias in a model with a lagged dependent variable and fixed effects?

Accepted Answer

A)  Large number of observations for each period. 
B)  Large number of time periods. 
C)  A large coefficient on the lagged dependent variable. 
D)  Using multiple lags (e.g., the dependent variable from the previous period and the period before that). 
A)  Large number of observations for each period. 
B)  Large number of time periods. 
C)  A large coefficient on the lagged dependent variable. 
D)  Using multiple lags (e.g., the dependent variable from the previous period and the period before that).

Question 6

Random effects models are appropriate only when their coefficients are similar to coefficients produced by fixed effects models.

Accepted Answer

A) True 
 B)False

Question 7

Suppose that we are comparing results in the following two models
Model A: Yit = 0.9Yi,t-1 + 2X1it
Model A: Yit = 0.8Yi,t-1 + 2X1it
Which of the following is true?

Accepted Answer

A)  The long-term effect of a one unit change in X will be 10 times bigger in Model A than in Model 
B)  The long-term effect of a one unit change in X will be 10 percent bigger in Model A than in Model 
C)  The short-term effect of a one unit change in X will be bigger in Model A than in Model 
D)  The short-term effect of a one unit change in X will be smaller in Model A than in Model 
A)  The long-term effect of a one unit change in X will be 10 times bigger in Model A than in Model 
B)  The long-term effect of a one unit change in X will be 10 percent bigger in Model A than in Model 
C)  The short-term effect of a one unit change in X will be bigger in Model A than in Model 
D)  The short-term effect of a one unit change in X will be smaller in Model A than in Model

Question 8

Autocorrelation causes bias in a model with fixed effects.

Accepted Answer

A) True 
 B)False

Question 9

Describe when it is appropriate to use random effects models.

Accepted Answer

Random effects models require that the a

Question 10

Explain why a model with a lagged dependent variable and fixed effects will be biased.

Accepted Answer

The fixed effects model with a lagged de

Question 11

Describe the potential trade-off between bias and accuracy using an example of an estimator that is biased, but accurate and one that is unbiased, but inaccurate.

Accepted Answer

It is possible to have an estimator that

Question 12

Under what conditions is the bias caused by autocorrelaed errors in a fixed effect model the most severe?

Accepted Answer

A)  We have many observations for each unit. 
B)  We have few observations for each unit. 
C)  We have many observations for each time period. 
D)  We have few observations for each time period. 
A)  We have many observations for each unit. 
B)  We have few observations for each unit. 
C)  We have many observations for each time period. 
D)  We have few observations for each time period.

Question 13

Random effects models are biased if the random effects error term is correlated with X.

Accepted Answer

A) True 
 B)False

Question 14

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

Accepted Answer

A)  Autocorrelation gets bigger. 
B)  Autocorrelation gets smaller. 
C)  Autocorrelation stays the same. 
D)  We cannot test for autocorrelation when a lagged dependent variable is included in the model. 
A)  Autocorrelation gets bigger. 
B)  Autocorrelation gets smaller. 
C)  Autocorrelation stays the same. 
D)  We cannot test for autocorrelation when a lagged dependent variable is included in the model.

Question 15

A biased coefficient is always worse than an unbiased coefficient.

Accepted Answer

A) True 
 B)False

Question 16

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

Accepted Answer

A)  Autocorrelation gets bigger. 
B)  Autocorrelation gets smaller. 
C)  Autocorrelation stays the same. 
D)  We cannot test for autocorrelation when a lagged dependent variable is included in the model. 
A)  Autocorrelation gets bigger. 
B)  Autocorrelation gets smaller. 
C)  Autocorrelation stays the same. 
D)  We cannot test for autocorrelation when a lagged dependent variable is included in the model.

Question 17

Which of the following is a proper procedure for testing for autocorrelation in a panel data model without a lagged dependent variable?

Accepted Answer

A)  Run a model that does not address autocorrelation and test for autocorrelated errors based on the residuals from that model. 
B)  Run a $\rho$-transformed model and test for autocorrelated errors based on the residuals from that model. 
C)  Assess whether there is a different intercept for each unit. 
D)  Run a probit or logit model and test for autocorrelated errors based on the residuals from that model. 
A)  Run a model that does not address autocorrelation and test for autocorrelated errors based on the residuals from that model. 
B)  Run a $\rho$-transformed model and test for autocorrelated errors based on the residuals from that model. 
C)  Assess whether there is a different intercept for each unit. 
D)  Run a probit or logit model and test for autocorrelated errors based on the residuals from that model.

Explain why it is important to test for autocorrelation when using a model with a lagged dependent variable and no fixed effects.

Suppose that we have the following results from a model with lagged dependent variable Y_i_t = 0.9Y_i_,t-1 + 2X_1i_t Which of the following is most accurate?

Autocorrelation causes bias in a model without fixed effects.

OLS coefficients are biased when there is a lagged dependent variable and fixed effects in the same model. Which of the following statements is most accurate about using OLS when there is a lagged dependent variable and fixed effects in the same model?

What factor is associated with lower bias in a model with a lagged dependent variable and fixed effects?

Random effects models are appropriate only when their coefficients are similar to coefficients produced by fixed effects models.

Suppose that we are comparing results in the following two models Model A: Y_i_t = 0.9Y_i_,t-1 + 2X_1i_t Model A: Y_i_t = 0.8Y_i_,t-1 + 2X_1i_t Which of the following is true?

Autocorrelation causes bias in a model with fixed effects.

Describe when it is appropriate to use random effects models.

Explain why a model with a lagged dependent variable and fixed effects will be biased.

Describe the potential trade-off between bias and accuracy using an example of an estimator that is biased, but accurate and one that is unbiased, but inaccurate.

Under what conditions is the bias caused by autocorrelaed errors in a fixed effect model the most severe?

Random effects models are biased if the random effects error term is correlated with X.

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

A biased coefficient is always worse than an unbiased coefficient.

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

Which of the following is a proper procedure for testing for autocorrelation in a panel data model without a lagged dependent variable?

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

Instrumental Variables: Using Exogenous Variation to Fight Endogen

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

Filters

Exam 15: Advanced Panel Data

Explain why it is important to test for autocorrelation when using a model with a lagged dependent variable and no fixed effects.

Suppose that we have the following results from a model with lagged dependent variable Yit = 0.9Yi,t-1 + 2X1it Which of the following is most accurate?

Autocorrelation causes bias in a model without fixed effects.

OLS coefficients are biased when there is a lagged dependent variable and fixed effects in the same model. Which of the following statements is most accurate about using OLS when there is a lagged dependent variable and fixed effects in the same model?

What factor is associated with lower bias in a model with a lagged dependent variable and fixed effects?

Random effects models are appropriate only when their coefficients are similar to coefficients produced by fixed effects models.

Suppose that we are comparing results in the following two models Model A: Yit = 0.9Yi,t-1 + 2X1it Model A: Yit = 0.8Yi,t-1 + 2X1it Which of the following is true?

Autocorrelation causes bias in a model with fixed effects.

Describe when it is appropriate to use random effects models.

Explain why a model with a lagged dependent variable and fixed effects will be biased.

Describe the potential trade-off between bias and accuracy using an example of an estimator that is biased, but accurate and one that is unbiased, but inaccurate.

Under what conditions is the bias caused by autocorrelaed errors in a fixed effect model the most severe?

Random effects models are biased if the random effects error term is correlated with X.

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

A biased coefficient is always worse than an unbiased coefficient.

What usually happens to autocorrelation when we add a lagged dependent variable to the model?

Which of the following is a proper procedure for testing for autocorrelation in a panel data model without a lagged dependent variable?

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

Instrumental Variables: Using Exogenous Variation to Fight Endogen

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

Filters

Suppose that we have the following results from a model with lagged dependent variable Y_i_t = 0.9Y_i_,t-1 + 2X_1i_t Which of the following is most accurate?

Suppose that we are comparing results in the following two models Model A: Y_i_t = 0.9Y_i_,t-1 + 2X_1i_t Model A: Y_i_t = 0.8Y_i_,t-1 + 2X_1i_t Which of the following is true?