Question 1

Randomized,controlled experiments are needed to accurately measure treatment effects without

Accepted Answer

A) the expense of having to treat everyone. 
B) the risk of discrimination bias. 
C) exposing everyone to untested treatments. 
D) selection bias. 
A) the expense of having to treat everyone. 
B) the risk of discrimination bias. 
C) exposing everyone to untested treatments. 
D) selection bias. D

Question 2

The Chow test is a specific application of a(n)

Accepted Answer

A) z-test 
B) $\chi$ 2 test 
C) F-test 
D) t-test 
A) z-test 
B) $\chi$ 2 test 
C) F-test 
D) t-test C

Question 3

When you have a multiple regression model with a binary dependent variable it is a __________.

Accepted Answer

A) dichotomous model 
B) Bernoulli model 
C) Linear Probability model 
D) prediction model 
A) dichotomous model 
B) Bernoulli model 
C) Linear Probability model 
D) prediction model C

Question 4

The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = $\beta$1 + $\beta$2 MALE + $\beta$3 PARTY + $\beta$4 MARRIED + $\beta$5 KIDS
Where
INCUMBENT = 1 if the voter votes for them,0 otherwise,
MALE = 1 if the voter is a male,
PARTY indicates the voter is registered with the same political party as the incumbent,
MARRIED = 1 for married voters,0 otherwise,and
KIDS is the number of school age kids living in the voter's house.
How should we interpret $\beta$4?

Accepted Answer

A) the likelihood the incumbent candidate is married 
B) the percentage of married voters who vote for the incumbent 
C) the probability a married person is registered to vote 
D) the difference in probability a married voter will vote for the incumbent as opposed to an unmarried voter 
A) the likelihood the incumbent candidate is married 
B) the percentage of married voters who vote for the incumbent 
C) the probability a married person is registered to vote 
D) the difference in probability a married voter will vote for the incumbent as opposed to an unmarried voter

Question 5

Which of the following wage premia is modeled with an indicator variable that shifts the intercept?

Accepted Answer

A) height 
B) gender 
C) education 
D) weight 
A) height 
B) gender 
C) education 
D) weight

Question 6

The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = $\beta$1 + $\beta$2 MALE + $\beta$3 PARTY + $\beta$4 MARRIED + $\beta$5 KIDS
Where
INCUMBENT = 1 if the voter votes for them,0 otherwise.
MALE = 1 if the voter is a male.
PARTY indicates the voter is registered with the same political party as the incumbent.
MARRIED = 1 for married voters,0 otherwise.
KIDS is the number of school age kids living in the voter's house.
If you believe marriage affects male and female voters differently,which variable should you add to the economic model to allow you to test the hypothesis?

Accepted Answer

A) MALE * PARTY 
B) MALE * MARRIED 
C) MARRIED * KIDS 
D) MARRIED * PARTY 
A) MALE * PARTY 
B) MALE * MARRIED 
C) MARRIED * KIDS 
D) MARRIED * PARTY

Question 7

The following Mincer equation has been used to estimate wages: ln (Y)= ln (Yo)+ $\beta$2EDU + $\beta$3 EXPER + $\beta$4 EXPER2 + e
Where Y is income,Y0 is income of someone with no education or experience,EDU is years of education and EXPER is experience in the field.If you suspect males earn higher wages than females and that the wage difference increases with education how would you adjust the econometric model to estimate wages?

Accepted Answer

A) include a binary variable for gender,MALE 
B) include an interaction term equal to MALE* EXPER 
C) include an indicator variable for MALE and one for FEMALE 
D) include a binary variable for MALE and an interaction term equal to MALE * EXPER 
A) include a binary variable for gender,MALE 
B) include an interaction term equal to MALE* EXPER 
C) include an indicator variable for MALE and one for FEMALE 
D) include a binary variable for MALE and an interaction term equal to MALE * EXPER

Question 8

Treatment effects are best estimated using data from

Accepted Answer

A) randomized,controlled experiments. 
B) subjects that have already undergone the risky treatment. 
C) people most in need of the treatments. 
D) natural or quasi-experiments. 
A) randomized,controlled experiments. 
B) subjects that have already undergone the risky treatment. 
C) people most in need of the treatments. 
D) natural or quasi-experiments.

Question 9

When certain characteristics cause a person to choose to be in a treatment group,selection bias can be overcome by using

Accepted Answer

A) conditional randomization and fixed effects. 
B) difference in differences estimation. 
C) larger sample sizes. 
D) quasi-experiments. 
A) conditional randomization and fixed effects. 
B) difference in differences estimation. 
C) larger sample sizes. 
D) quasi-experiments.

Question 10

Which of the following terms is NOT commonly used to refer to an indicator variable?

Accepted Answer

A) dummy 
B) binary 
C) dichotomous 
D) digital 
A) dummy 
B) binary 
C) dichotomous 
D) digital

Question 11

What benefit is gained by estimating treatment effects with fixed effects using panel data?

Accepted Answer

A) it controls for unobserved,individual characteristics 
B) it controls for changes in individuals over time 
C) it allows the treatment effect to vary with the length of treatment 
D) it "fixes" the treatment to the same time for each individual 
A) it controls for unobserved,individual characteristics 
B) it controls for changes in individuals over time 
C) it allows the treatment effect to vary with the length of treatment 
D) it "fixes" the treatment to the same time for each individual

Question 12

Treatment effects can be estimated from natural or quasi-experiments using which estimator?

Accepted Answer

A) Restricted least squares 
B) Difference-in-differences estimator 
C) Fixed effects 
D) Quasi-Likelihood 
A) Restricted least squares 
B) Difference-in-differences estimator 
C) Fixed effects 
D) Quasi-Likelihood

Question 13

A large company is accused of gender discrimination in wages.The following model has been estimated from the company's human resource information ln(WAGE)= 1.439 + .0834 EDU + .0512 EXPER + .1932 MALE
Where WAGE is hourly wage,EDU is years of education,EXPER is years of relevant experience,and MALE indicates the employee is male.How much more do men at the firm earn,on average?

Accepted Answer

A) $1.21 per hour more than females 
B) 19.32% more than females 
C) $19.32 per hour 
D) $19,320 more per year than females 
A) $1.21 per hour more than females 
B) 19.32% more than females 
C) $19.32 per hour 
D) $19,320 more per year than females

Question 14

Estimating treatment effects using difference-in-differences requires what kind of data?

Accepted Answer

A) aggregate measures over time 
B) time-series data spanning the treatment length 
C) paired,panel data 
D) cross-section spanning the treated population 
A) aggregate measures over time 
B) time-series data spanning the treatment length 
C) paired,panel data 
D) cross-section spanning the treated population

Question 15

Which of the following variables is not necessary in order to estimate treatment effects using difference-in-differences?

Accepted Answer

A) a treatment/control indicator 
B) pre-treatment / post-treatment indicator 
C) treatment group * treatment time interaction term 
D) post-treatment performance 
A) a treatment/control indicator 
B) pre-treatment / post-treatment indicator 
C) treatment group * treatment time interaction term 
D) post-treatment performance

Question 16

.A large company is accused of gender discrimination in wages.The following model has been estimated from the company's human resource information ln(WAGE)= 1.439 + .0834 EDU + .0512 EXPER + .1932 MALE
Where WAGE is hourly wage,EDU is years of education,EXPER is years of relevant experience,and MALE indicates the employee is male.What hypothesis would you test to determine if the discrimination claim is valid?

Accepted Answer

A) H0:$\beta$MALE = 0 ;H1: $\beta$MALE ≥ 0 
B) H0:$\beta$MALE = $\beta$EDU = $\beta$EXPER = 0 ;H1: $\beta$MALE ≠ 0 and $\beta$EDU ≠ 0 and $\beta$EXPER ≠ 0 
C) H0:$\beta$MALE = $\beta$EDU = $\beta$EXPER = 0 ;H1: $\beta$MALE ≠ 0 or $\beta$EDU ≠ 0 or $\beta$EXPER ≠ 0 
D) H0:$\beta$MALE ≤ $\beta$EDU or $\beta$MALE ≤ $\beta$EXPER  ;H1: $\beta$MALE > $\beta$EDU or $\beta$MALE > $\beta$EXPER 
A) H0:$\beta$MALE = 0 ;H1: $\beta$MALE ≥ 0 
B) H0:$\beta$MALE = $\beta$EDU = $\beta$EXPER = 0 ;H1: $\beta$MALE ≠ 0 and $\beta$EDU ≠ 0 and $\beta$EXPER ≠ 0 
C) H0:$\beta$MALE = $\beta$EDU = $\beta$EXPER = 0 ;H1: $\beta$MALE ≠ 0 or $\beta$EDU ≠ 0 or $\beta$EXPER ≠ 0 
D) H0:$\beta$MALE ≤ $\beta$EDU or $\beta$MALE ≤ $\beta$EXPER  ;H1: $\beta$MALE > $\beta$EDU or $\beta$MALE > $\beta$EXPER

Question 17

If you perform a Chow test to compare two regressions and reject the null hypothesis,what should you conclude?

Accepted Answer

A) there is not sufficient evidence that the regressions are significantly different 
B) the regression equations are statistically different 
C) the regression equations are equivalent 
D) it depends on how you set up the null hypothesis 
A) there is not sufficient evidence that the regressions are significantly different 
B) the regression equations are statistically different 
C) the regression equations are equivalent 
D) it depends on how you set up the null hypothesis

Question 18

The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = $\beta$1 + $\beta$2 MALE + $\beta$3 PARTY + $\beta$4 MARRIED + $\beta$5 KIDS
Where
INCUMBENT = 1 if the voter votes for them,0 otherwise,
MALE = 1 if the voter is a male,
PARTY indicates the voter is registered with the same political party as the incumbent,
MARRIED = 1 for married voters,0 otherwise,and
KIDS is the number of school age kids living in the voter's house.
If you hypothesize males and females might have a different willingness to vote for a candidate registered with a different political party,which variable should you add to the economic model to allow you to test the hypothesis?

Accepted Answer

A) MALE * PARTY 
B) MALE * MARRIED 
C) MARRIED * KIDS 
D) MARRIED * PARTY 
A) MALE * PARTY 
B) MALE * MARRIED 
C) MARRIED * KIDS 
D) MARRIED * PARTY

Question 19

The following economic model predicts whether a voter will vote for an incumbent school board member INCUMBENT = $\beta$1 + $\beta$2 MALE + $\beta$3 PARTY + $\beta$4 MARRIED + $\beta$5 KIDS
Where
INCUMBENT = 1 if the voter votes for them,0 otherwise,
MALE = 1 if the voter is a male,
PARTY indicates the voter is registered with the same political party as the incumbent,
MARRIED = 1 for married voters,0 otherwise,and
KIDS is the number of school age kids living with the voter.
What is the probability that a married female without kids who is not registered with a political party will vote for the incumbent?

Accepted Answer

A) $\beta$1 + $\beta$4 
B) $\beta$1 
C) $\beta$1 + $\beta$2 + $\beta$3 + $\beta$5 
D) $\beta$2 + $\beta$3 + $\beta$5 
A) $\beta$1 + $\beta$4 
B) $\beta$1 
C) $\beta$1 + $\beta$2 + $\beta$3 + $\beta$5 
D) $\beta$2 + $\beta$3 + $\beta$5

Randomized,controlled experiments are needed to accurately measure treatment effects without

The Chow test is a specific application of a(n)

When you have a multiple regression model with a binary dependent variable it is a __________.

Which of the following wage premia is modeled with an indicator variable that shifts the intercept?

Treatment effects are best estimated using data from

When certain characteristics cause a person to choose to be in a treatment group,selection bias can be overcome by using

Which of the following terms is NOT commonly used to refer to an indicator variable?

What benefit is gained by estimating treatment effects with fixed effects using panel data?

Treatment effects can be estimated from natural or quasi-experiments using which estimator?

Estimating treatment effects using difference-in-differences requires what kind of data?

Which of the following variables is not necessary in order to estimate treatment effects using difference-in-differences?

If you perform a Chow test to compare two regressions and reject the null hypothesis,what should you conclude?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters

Exam 7: Using Indicator Variables

Randomized,controlled experiments are needed to accurately measure treatment effects without

The Chow test is a specific application of a(n)

When you have a multiple regression model with a binary dependent variable it is a __________.

Which of the following wage premia is modeled with an indicator variable that shifts the intercept?

Treatment effects are best estimated using data from

When certain characteristics cause a person to choose to be in a treatment group,selection bias can be overcome by using

Which of the following terms is NOT commonly used to refer to an indicator variable?

What benefit is gained by estimating treatment effects with fixed effects using panel data?

Treatment effects can be estimated from natural or quasi-experiments using which estimator?

Estimating treatment effects using difference-in-differences requires what kind of data?

Which of the following variables is not necessary in order to estimate treatment effects using difference-in-differences?

If you perform a Chow test to compare two regressions and reject the null hypothesis,what should you conclude?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters