Question 1

For quasi-experiments,

Accepted Answer

A) there is a particularly important potential threat to internal validity, namely whether the "as if" randomization in fact can be treated reliably as true randomization. 
B) there are the same threats to internal validity as for true randomized controlled experiments, without modifications. 
C) there is little threat to external validity, since the populations are typically already different. 
D) OLS estimation should not be used. 
A) there is a particularly important potential threat to internal validity, namely whether the "as if" randomization in fact can be treated reliably as true randomization. 
B) there are the same threats to internal validity as for true randomized controlled experiments, without modifications. 
C) there is little threat to external validity, since the populations are typically already different. 
D) OLS estimation should not be used. A

Question 2

The following is not a threat to external validity:

Accepted Answer

A) the experimental sample is not representative of the population of interest. 
B) the treatment being studied is not representative of the treatment that would be implemented more broadly. 
C) experimental participants are volunteers. 
D) partial compliance with the treatment protocol. 
A) the experimental sample is not representative of the population of interest. 
B) the treatment being studied is not representative of the treatment that would be implemented more broadly. 
C) experimental participants are volunteers. 
D) partial compliance with the treatment protocol. D

Question 3

Assume that data are available on other characteristics of the subjects that are relevant to determining the experimental outcome. Then including these determinants explicitly results in

Accepted Answer

A) the limited dependent variable model. 
B) the differences in means test. 
C) the multiple regression model. 
D) large scale equilibrium effects. 
A) the limited dependent variable model. 
B) the differences in means test. 
C) the multiple regression model. 
D) large scale equilibrium effects. C

Question 4

Program evaluation

Accepted Answer

A) is conducted for most departments in your university/college about every seven years. 
B) is the field of study that concerns estimating the effect of a program, policy, or some other intervention or "treatment." 
C) tries to establish whether EViews, SAS or Stata work best for your econometrics course. 
D) establishes rating systems for television programs in a controlled experiment framework. 
A) is conducted for most departments in your university/college about every seven years. 
B) is the field of study that concerns estimating the effect of a program, policy, or some other intervention or "treatment." 
C) tries to establish whether EViews, SAS or Stata work best for your econometrics course. 
D) establishes rating systems for television programs in a controlled experiment framework.

Question 5

The following estimation methods should not be used to test for randomization when Xi, is binary:

Accepted Answer

A) linear probability model (OLS)with homoskedasticity-only standard errors. 
B) probit. 
C) logit. 
D) linear probability model (OLS)with heteroskedasticity-robust standard errors. 
A) linear probability model (OLS)with homoskedasticity-only standard errors. 
B) probit. 
C) logit. 
D) linear probability model (OLS)with heteroskedasticity-robust standard errors.

Question 6

Your textbooks gives several examples of quasi experiments that were conducted. The following is not an example of a quasi experiment:

Accepted Answer

A) labor market effects of immigration. 
B) effects on civilian earnings of military service. 
C) the effect of cardiac catheterization. 
D) the effect of unemployment on the inflation rate. 
A) labor market effects of immigration. 
B) effects on civilian earnings of military service. 
C) the effect of cardiac catheterization. 
D) the effect of unemployment on the inflation rate.

Question 7

Causal effects that depend on the value of an observable variable, say Wi,

Accepted Answer

A) cannot be estimated. 
B) can be estimate by interacting the treatment variable with Wi. 
C) result in the OLS estimator being inefficient. 
D) requires use of homoskedasticity-only standard errors. 
A) cannot be estimated. 
B) can be estimate by interacting the treatment variable with Wi. 
C) result in the OLS estimator being inefficient. 
D) requires use of homoskedasticity-only standard errors.

Question 8

To test for randomization when Xi is binary,

Accepted Answer

A) you regress Xi, on all W's and compute the F-statistic for testing that all the coefficients on the W's are zero. (The W's measure characteristics of individuals, and these are not affected by the treatment.) 
B) is not possible, since binary variables can only be regressors. 
C) requires reordering the observations randomly and re-estimating the model. If the coefficients remain the same, then this is evidence of randomization. 
D) requires seeking external validity for your study. 
A) you regress Xi, on all W's and compute the F-statistic for testing that all the coefficients on the W's are zero. (The W's measure characteristics of individuals, and these are not affected by the treatment.) 
B) is not possible, since binary variables can only be regressors. 
C) requires reordering the observations randomly and re-estimating the model. If the coefficients remain the same, then this is evidence of randomization. 
D) requires seeking external validity for your study.

Question 9

Experimental data are often

Accepted Answer

A) observational data. 
B) binary data, in that the subject either does or does not respond to the treatment. 
C) panel data. 
D) time series data. 
A) observational data. 
B) binary data, in that the subject either does or does not respond to the treatment. 
C) panel data. 
D) time series data.

Question 10

The Hawthorne effect refers to

Accepted Answer

A) subjects dropping out of the study after being randomly assigned to the treatment or control group. 
B) the failure of individuals to follow completely the randomized treatment protocol. 
C) the phenomenon that subjects in an experiment can change their behavior merely by being included in the experiment. 
D) assigning individuals, in part, as a result of their characteristics or preferences. 
A) subjects dropping out of the study after being randomly assigned to the treatment or control group. 
B) the failure of individuals to follow completely the randomized treatment protocol. 
C) the phenomenon that subjects in an experiment can change their behavior merely by being included in the experiment. 
D) assigning individuals, in part, as a result of their characteristics or preferences.

Question 11

A repeated cross-sectional data set

Accepted Answer

A) is also referred to as panel data. 
B) is a collection of cross-sectional data sets, where each cross-sectional data set corresponds to a different time period. 
C) samples identical entities at least twice. 
D) is typically used for estimating the following regression model Yit = β0 + β1Xit + β2W1,it + ... + β1+ rWr,it + uit 
A) is also referred to as panel data. 
B) is a collection of cross-sectional data sets, where each cross-sectional data set corresponds to a different time period. 
C) samples identical entities at least twice. 
D) is typically used for estimating the following regression model Yit = β0 + β1Xit + β2W1,it + ... + β1+ rWr,it + uit

Question 12

Experimental effects, such as the Hawthorne effect,

Accepted Answer

A) generally are not germane in quasi-experiments. 
B) typically require instrumental variable estimation in quasi-experiments. 
C) can be dealt with using binary variables in quasi-experiments. 
D) are the most important threat to internal validity in quasi-experiments. 
A) generally are not germane in quasi-experiments. 
B) typically require instrumental variable estimation in quasi-experiments. 
C) can be dealt with using binary variables in quasi-experiments. 
D) are the most important threat to internal validity in quasi-experiments.

Question 13

In the context of a controlled experiment, consider the simple linear regression formulation Yi = ?0 + ?1Xi + ui. Let the Yi be the outcome, Xi the treatment level when the treatment is binary, and ui contain all the additional determinants of the outcome. Then calling $\hat \beta _ { 1 }$ a differences estimator

Accepted Answer

A) makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group. 
B) and $\hat \beta _ { 0 }$ the level estimator is standard terminology in randomized controlled experiments. 
C) does not make sense, since neither Y nor X are in differences. 
D) is not quite accurate since it is actually the derivative of Y on X. 
A) makes sense since it is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group. 
B) and $\hat \beta _ { 0 }$ the level estimator is standard terminology in randomized controlled experiments. 
C) does not make sense, since neither Y nor X are in differences. 
D) is not quite accurate since it is actually the derivative of Y on X.

Question 14

In a quasi-experiment

Accepted Answer

A) quasi differences are used, i.e., instead of ?Y you need to use ( $\bar { Y } \text { after }$ - ? × $\bar { Y } \text { before }$ ), where 0 < ? < 1. 
B) randomness is introduced by variations in individual circumstances that make it appear as if the treatment is randomly assigned. 
C) the causal effect has to be estimated through quasi maximum likelihood estimation. 
D) the t-statistic is no longer normally distributed in large samples. 
A) quasi differences are used, i.e., instead of ?Y you need to use ( $\bar { Y } \text { after }$ - ? × $\bar { Y } \text { before }$ ), where 0 < ? < 1. 
B) randomness is introduced by variations in individual circumstances that make it appear as if the treatment is randomly assigned. 
C) the causal effect has to be estimated through quasi maximum likelihood estimation. 
D) the t-statistic is no longer normally distributed in large samples.

Question 15

In the context of a controlled experiment, consider the simple linear regression formulation Yi = ?0 + ?1Xi + ui. Let the Yi be the outcome, Xi the treatment level, and ui contain all the additional determinants of the outcome. Then

Accepted Answer

A) the OLS estimator of the slope will be inconsistent in the case of a randomly assigned Xi since there are omitted variables present. 
B) Xi and ui will be independently distributed if the Xi be are randomly assigned. 
C) ?0 represents the causal effect of X on Y when X is zero. 
D) E(Y | X = 0)is the expected value for the treatment group. 
A) the OLS estimator of the slope will be inconsistent in the case of a randomly assigned Xi since there are omitted variables present. 
B) Xi and ui will be independently distributed if the Xi be are randomly assigned. 
C) ?0 represents the causal effect of X on Y when X is zero. 
D) E(Y | X = 0)is the expected value for the treatment group.

Question 16

With panel data, the causal effect

Accepted Answer

A) cannot be estimated since correlation does not imply causation. 
B) is typically estimated using the probit regression model. 
C) can be estimated using the "differences-in-differences" estimator. 
D) can be estimated by looking at the difference between the treatment and the control group after the treatment has taken place. 
A) cannot be estimated since correlation does not imply causation. 
B) is typically estimated using the probit regression model. 
C) can be estimated using the "differences-in-differences" estimator. 
D) can be estimated by looking at the difference between the treatment and the control group after the treatment has taken place.

Question 17

The following does not represent a threat to internal validity of randomized controlled experiments:

Accepted Answer

A) attrition. 
B) failure to follow the treatment protocol. 
C) experimental effects. 
D) a large sample size. 
A) attrition. 
B) failure to follow the treatment protocol. 
C) experimental effects. 
D) a large sample size.

Question 18

All of the following are reasons for using the differences estimator with additional regressors, with the exception of

Accepted Answer

A) efficiency. 
B) providing a check for randomization. 
C) providing an adjustment for "conditional" randomization. 
D) making the difference estimator easier to calculate than in the case of the differences estimator without the additional regressors. 
A) efficiency. 
B) providing a check for randomization. 
C) providing an adjustment for "conditional" randomization. 
D) making the difference estimator easier to calculate than in the case of the differences estimator without the additional regressors.

Question 19

The following are reasons for studying randomized controlled experiment in an econometrics course, with the exception of

Accepted Answer

A) at a conceptual level, the notion of an ideal randomized controlled experiment provides a benchmark against which to judge estimates of causal effects in practice. 
B) when experiments are actually conducted, their results can be very influential, so it is important to understand the limitations and threats to validity of actual experiments as well as their strength. 
C) randomized controlled experiments in economics are common. 
D) external circumstances sometimes produce what appears to be randomization. 
A) at a conceptual level, the notion of an ideal randomized controlled experiment provides a benchmark against which to judge estimates of causal effects in practice. 
B) when experiments are actually conducted, their results can be very influential, so it is important to understand the limitations and threats to validity of actual experiments as well as their strength. 
C) randomized controlled experiments in economics are common. 
D) external circumstances sometimes produce what appears to be randomization.

Question 20

Heterogeneous population

Accepted Answer

A) implies that heteroskedasticity-robust standard errors must be used. 
B) suggest that multiple characteristics must be used to describe the population. 
C) effects can be captured through interaction terms. 
D) refers to circumstances in which there is unobserved variation in the causal effect with the population. 
A) implies that heteroskedasticity-robust standard errors must be used. 
B) suggest that multiple characteristics must be used to describe the population. 
C) effects can be captured through interaction terms. 
D) refers to circumstances in which there is unobserved variation in the causal effect with the population.

For quasi-experiments,

The following is not a threat to external validity:

Assume that data are available on other characteristics of the subjects that are relevant to determining the experimental outcome. Then including these determinants explicitly results in

Program evaluation

The following estimation methods should not be used to test for randomization when X_i, is binary:

Your textbooks gives several examples of quasi experiments that were conducted. The following is not an example of a quasi experiment:

Causal effects that depend on the value of an observable variable, say W_i,

To test for randomization when X_i is binary,

Experimental data are often

The Hawthorne effect refers to

A repeated cross-sectional data set

Experimental effects, such as the Hawthorne effect,

In a quasi-experiment

In the context of a controlled experiment, consider the simple linear regression formulation Y_i = ?₀ + ?₁X_i + u_i. Let the Y_i be the outcome, X_i the treatment level, and u_i contain all the additional determinants of the outcome. Then

With panel data, the causal effect

The following does not represent a threat to internal validity of randomized controlled experiments:

All of the following are reasons for using the differences estimator with additional regressors, with the exception of

The following are reasons for studying randomized controlled experiment in an econometrics course, with the exception of

Heterogeneous population

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters

Exam 13: Experiments and Quasi-Experiments

For quasi-experiments,

The following is not a threat to external validity:

Assume that data are available on other characteristics of the subjects that are relevant to determining the experimental outcome. Then including these determinants explicitly results in

Program evaluation

The following estimation methods should not be used to test for randomization when Xi, is binary:

Your textbooks gives several examples of quasi experiments that were conducted. The following is not an example of a quasi experiment:

Causal effects that depend on the value of an observable variable, say Wi,

To test for randomization when Xi is binary,

Experimental data are often

The Hawthorne effect refers to

A repeated cross-sectional data set

Experimental effects, such as the Hawthorne effect,

In a quasi-experiment

In the context of a controlled experiment, consider the simple linear regression formulation Yi = ?0 + ?1Xi + ui. Let the Yi be the outcome, Xi the treatment level, and ui contain all the additional determinants of the outcome. Then

With panel data, the causal effect

The following does not represent a threat to internal validity of randomized controlled experiments:

All of the following are reasons for using the differences estimator with additional regressors, with the exception of

The following are reasons for studying randomized controlled experiment in an econometrics course, with the exception of

Heterogeneous population

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters

The following estimation methods should not be used to test for randomization when X_i, is binary:

Causal effects that depend on the value of an observable variable, say W_i,

To test for randomization when X_i is binary,

In the context of a controlled experiment, consider the simple linear regression formulation Y_i = ?₀ + ?₁X_i + u_i. Let the Y_i be the outcome, X_i the treatment level, and u_i contain all the additional determinants of the outcome. Then