Question 1

All of the following variables are likely to be limited dependent variables except for which one?

Accepted Answer

A)  Consumer trips that include an ice cream purchase 
B)  Seasons in which a team wins their national championship 
C)  Average wholesale electricity price for state of Indiana 
D)  Whether a mortgage applicant is approved or not 
A)  Consumer trips that include an ice cream purchase 
B)  Seasons in which a team wins their national championship 
C)  Average wholesale electricity price for state of Indiana 
D)  Whether a mortgage applicant is approved or not C

Question 2

The primary distinction between a linear probability model and linear regression model is:

Accepted Answer

A)  you can't interpret the coefficients in a linear probability model. 
B)  a linear probability model never suffers from heteroscedasticity. 
C)  the standard errors of a linear probability model will be larger. 
D)  the outcome is dichotomous in a linear probability model. 
A)  you can't interpret the coefficients in a linear probability model. 
B)  a linear probability model never suffers from heteroscedasticity. 
C)  the standard errors of a linear probability model will be larger. 
D)  the outcome is dichotomous in a linear probability model. D

Question 3

A latent variable is one that:

Accepted Answer

A)  can be used instead of an instrumental variable in two-stage least squares. 
B)  will cause multi-collinearity problems. 
C)  cannot be observed, but information about it can be inferred from other observed variables. 
D)  will create endogeneity problems in your regression model. 
A)  can be used instead of an instrumental variable in two-stage least squares. 
B)  will cause multi-collinearity problems. 
C)  cannot be observed, but information about it can be inferred from other observed variables. 
D)  will create endogeneity problems in your regression model. C

Question 4

Which of the following correctly summarizes one of the differences in calculating marginal effects of probit/logit models relative to linear probability models?

Accepted Answer

A)  Probit/logit models' marginal effects are causal; linear probability models are not. 
B)  Probit/logit models' marginal effects will not be constant for all values of X, while (strictly) linear probability models' marginal effects will be constant. 
C)  Probit/logit marginal effects cannot be positive since predictions need to be between zero and 1, while linear probability models can be positive. 
D)  Probit/logit marginal effects are stable, while linear probability models tend to be noisier. 
A)  Probit/logit models' marginal effects are causal; linear probability models are not. 
B)  Probit/logit models' marginal effects will not be constant for all values of X, while (strictly) linear probability models' marginal effects will be constant. 
C)  Probit/logit marginal effects cannot be positive since predictions need to be between zero and 1, while linear probability models can be positive. 
D)  Probit/logit marginal effects are stable, while linear probability models tend to be noisier.

Question 5

In the standard regression formula of Yi = β0 + β1Xi + Ui, which object is most closely tied to the marginal effect of X on Y?

Accepted Answer

A)  β0 
B)  Yi 
C)  β1 
D)  Ui 
A)  β0 
B)  Yi 
C)  β1 
D)  Ui

Question 6

Which of the following limitations of the linear probability is not problematic in the event that the only treatment variable in your regression (X or right-hand side), is a binary variable?

Accepted Answer

A)  The errors of the model have to be heteroscedastic. 
B)  The errors of the model have to be homoscedastic. 
C)  The predictions might not fall between zero and 1. 
D)  The coefficients might be negative. 
A)  The errors of the model have to be heteroscedastic. 
B)  The errors of the model have to be homoscedastic. 
C)  The predictions might not fall between zero and 1. 
D)  The coefficients might be negative.

Question 7

In estimating the linear probability model for whether an individual clicked through on an advertisement based off whether the individual is on a mobile device or not, which of the following conditions may not hold from the resulting regression (ClickThroughi = β0 + β1Mobilei + Ui)?

Accepted Answer

A)  The sum of the residuals will be zero. 
B)  The sum of the residuals for the mobile observations will be zero. 
C)  The sum of the residuals for the non-mobile observations will be zero. 
D)  The sum of the predictions will be zero. 
A)  The sum of the residuals will be zero. 
B)  The sum of the residuals for the mobile observations will be zero. 
C)  The sum of the residuals for the non-mobile observations will be zero. 
D)  The sum of the predictions will be zero.

Question 8

How does the interpretation of the coefficient on the income to debt ratio change in the linear probability model of whether a mortgage applicant was approved for a loan or not if the outcome variable was Deniedi instead of Approvedi?

Accepted Answer

A)  It won't change. 
B)  The coefficients will be the same, but the standard errors will change. 
C)  One will be a model of causality, the other will be purely about correlation. 
D)  One coefficient will be the change in probability of being denied, while the other will be the change in probability of being approved. 
A)  It won't change. 
B)  The coefficients will be the same, but the standard errors will change. 
C)  One will be a model of causality, the other will be purely about correlation. 
D)  One coefficient will be the change in probability of being denied, while the other will be the change in probability of being approved.

Question 9

After estimating a probit model for the likelihood of a website visitor clicking through conditional on if the visit occurred on a weekday or not, you get the following results: ClickThroughi = -1.2(0.4) + 0.8(0.2)WeekDayi, where standard errors are reported in parenthesis. What would be the calculation that yields the marginal effect of a visit moving from a weekend to a weekday on click-throughs?

Accepted Answer

A)  -1.2 + 0.8 = -0.4 
B)  0.8 
C)  Φ(-1.2 + 0.8) - Φ(-1.2) 
D)  Φ(-1.2) 
A)  -1.2 + 0.8 = -0.4 
B)  0.8 
C)  Φ(-1.2 + 0.8) - Φ(-1.2) 
D)  Φ(-1.2)

Question 10

A shortcoming of potentially using the probit/logit model when attempting to identify causal effects can include:

Accepted Answer

A)  software to estimate the probit/logit model is not widely available. 
B)  the use of instrumental variables with probit/logit is difficult. 
C)  probit/logit models have a difficult time incorporating binary control variables. 
D)  hypothesis testing is all but impossible in probit/logit models. 
A)  software to estimate the probit/logit model is not widely available. 
B)  the use of instrumental variables with probit/logit is difficult. 
C)  probit/logit models have a difficult time incorporating binary control variables. 
D)  hypothesis testing is all but impossible in probit/logit models.

Question 11

The probit model assumes what sort of distribution for the unobservables?

Accepted Answer

A)  Standard normal distribution 
B)  T-distribution 
C)  Logistic distribution 
D)  The probit distribution 
A)  Standard normal distribution 
B)  T-distribution 
C)  Logistic distribution 
D)  The probit distribution

Question 12

After estimating a logit model for the likelihood of a website visitor clicking through conditional on if the visit occurred on a weekday or not, you get the following results: ClickThroughi = -1.2(0.4) + 0.8(0.2)WeekDayi, where standard errors are reported in parenthesis. What would be the calculation that yields the marginal effect of a visit moving from a weekend to a weekday on click-throughs?

Accepted Answer

A)  -1.2 + 0.8 = -0.4 
B)  0.8 
C)  $\frac { 1 } { 1 + e ^ { - 1.2 + 0.8 } }$ 
D)  $$\frac { 1 } { 1 + e ^ { - 1.2 + 0.8 } }$$ - $$\frac { 1 } { 1 + e ^ { - 1.2 } }$$ 
A)  -1.2 + 0.8 = -0.4 
B)  0.8 
C)  $\frac { 1 } { 1 + e ^ { - 1.2 + 0.8 } }$ 
D)  $$\frac { 1 } { 1 + e ^ { - 1.2 + 0.8 } }$$ - $$\frac { 1 } { 1 + e ^ { - 1.2 } }$$

Question 13

If one ran the regression on whether a mortgage applicant was approved for a loan or not (Approvedi) on their income to debt ratio, this would be an example of what sort of model?

Accepted Answer

A)  A two-stage least squares model 
B)  A fixed-effects model 
C)  A within estimator model 
D)  A linear probability model 
A)  A two-stage least squares model 
B)  A fixed-effects model 
C)  A within estimator model 
D)  A linear probability model

Question 14

A natural latent variable for a probit model for modeling the purchase of a good by consumers would be which of the following?

Accepted Answer

A)  Profits 
B)  Net revenues 
C)  Costs 
D)  Utility 
A)  Profits 
B)  Net revenues 
C)  Costs 
D)  Utility

Question 15

Which of the following variables would be natural outcomes for a linear probability model?

Accepted Answer

A)  Income 
B)  GDP 
C)  Wages 
D)  Click-through or no click-through 
A)  Income 
B)  GDP 
C)  Wages 
D)  Click-through or no click-through

Question 16

A common method to estimate probit and logit models is:

Accepted Answer

A)  moment conditions. 
B)  within estimator. 
C)  maximum likelihood estimation. 
D)  OLS. 
A)  moment conditions. 
B)  within estimator. 
C)  maximum likelihood estimation. 
D)  OLS.

Question 17

A marginal effect summarizes the:

Accepted Answer

A)  change in a coefficient from altering the sample size. 
B)  estimated size of the variance for the model residuals. 
C)  rate of change in the probability of a dichotomous dependent variable equaling one with a one-unit increase in an independent variable. 
D)  size of the p-value on a coefficient. 
A)  change in a coefficient from altering the sample size. 
B)  estimated size of the variance for the model residuals. 
C)  rate of change in the probability of a dichotomous dependent variable equaling one with a one-unit increase in an independent variable. 
D)  size of the p-value on a coefficient.

Question 18

Which of the following variables is most likely to be a limited dependent variable, assuming that each variable will be featured as a dependent variable?

Accepted Answer

A)  GDP growth 
B)  Change in the number of faulty units 
C)  Labor productivity 
D)  Weekly number of complaints 
A)  GDP growth 
B)  Change in the number of faulty units 
C)  Labor productivity 
D)  Weekly number of complaints

Question 19

If you are planning on running the regression model given by Tenurei = β0 + β1Salaryi + β2Years of Educationi + Ui, which of the following situations would cause this model to have a limited dependent variable?

Accepted Answer

A)  No one in the company earns more $130,000 a year. 
B)  Everyone in the company earns at least $65,000 a year. 
C)  No one can have negative years of education. 
D)  No one can have negative tenure at the company. 
A)  No one in the company earns more $130,000 a year. 
B)  Everyone in the company earns at least $65,000 a year. 
C)  No one can have negative years of education. 
D)  No one can have negative tenure at the company.

Question 20

One of the merits of the linear probability model is that it:

Accepted Answer

A)  is a stronger model of causality than two-stage least squares. 
B)  does not require defining a determining function. 
C)  is easy to summarize and use traditional linear regression model techniques. 
D)  does not suffer from small sample issues. 
A)  is a stronger model of causality than two-stage least squares. 
B)  does not require defining a determining function. 
C)  is easy to summarize and use traditional linear regression model techniques. 
D)  does not suffer from small sample issues.

All of the following variables are likely to be limited dependent variables except for which one?

The primary distinction between a linear probability model and linear regression model is:

A latent variable is one that:

Which of the following correctly summarizes one of the differences in calculating marginal effects of probit/logit models relative to linear probability models?

In the standard regression formula of Y_i = β₀ + β₁X_i + U_i, which object is most closely tied to the marginal effect of X on Y?

Which of the following limitations of the linear probability is not problematic in the event that the only treatment variable in your regression (X or right-hand side), is a binary variable?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off whether the individual is on a mobile device or not, which of the following conditions may not hold from the resulting regression (ClickThrough_i = β₀ + β₁Mobile_i + U_i)?

How does the interpretation of the coefficient on the income to debt ratio change in the linear probability model of whether a mortgage applicant was approved for a loan or not if the outcome variable was Denied_i instead of Approved_i?

A shortcoming of potentially using the probit/logit model when attempting to identify causal effects can include:

The probit model assumes what sort of distribution for the unobservables?

If one ran the regression on whether a mortgage applicant was approved for a loan or not (Approved_i) on their income to debt ratio, this would be an example of what sort of model?

A natural latent variable for a probit model for modeling the purchase of a good by consumers would be which of the following?

Which of the following variables would be natural outcomes for a linear probability model?

A common method to estimate probit and logit models is:

A marginal effect summarizes the:

Which of the following variables is most likely to be a limited dependent variable, assuming that each variable will be featured as a dependent variable?

If you are planning on running the regression model given by Tenure_i = β₀ + β₁Salary_i + β₂Years of Education_i + U_i, which of the following situations would cause this model to have a limited dependent variable?

One of the merits of the linear probability model is that it:

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Identification and Data Assessment

Filters

Exam 9: Prediction for a Dichotomous Variable

All of the following variables are likely to be limited dependent variables except for which one?

The primary distinction between a linear probability model and linear regression model is:

A latent variable is one that:

Which of the following correctly summarizes one of the differences in calculating marginal effects of probit/logit models relative to linear probability models?

In the standard regression formula of Yi = β0 + β1Xi + Ui, which object is most closely tied to the marginal effect of X on Y?

Which of the following limitations of the linear probability is not problematic in the event that the only treatment variable in your regression (X or right-hand side), is a binary variable?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off whether the individual is on a mobile device or not, which of the following conditions may not hold from the resulting regression (ClickThroughi = β0 + β1Mobilei + Ui)?

How does the interpretation of the coefficient on the income to debt ratio change in the linear probability model of whether a mortgage applicant was approved for a loan or not if the outcome variable was Deniedi instead of Approvedi?

A shortcoming of potentially using the probit/logit model when attempting to identify causal effects can include:

The probit model assumes what sort of distribution for the unobservables?

If one ran the regression on whether a mortgage applicant was approved for a loan or not (Approvedi) on their income to debt ratio, this would be an example of what sort of model?

A natural latent variable for a probit model for modeling the purchase of a good by consumers would be which of the following?

Which of the following variables would be natural outcomes for a linear probability model?

A common method to estimate probit and logit models is:

A marginal effect summarizes the:

Which of the following variables is most likely to be a limited dependent variable, assuming that each variable will be featured as a dependent variable?

If you are planning on running the regression model given by Tenurei = β0 + β1Salaryi + β2Years of Educationi + Ui, which of the following situations would cause this model to have a limited dependent variable?

One of the merits of the linear probability model is that it:

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Identification and Data Assessment

Filters

In the standard regression formula of Y_i = β₀ + β₁X_i + U_i, which object is most closely tied to the marginal effect of X on Y?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off whether the individual is on a mobile device or not, which of the following conditions may not hold from the resulting regression (ClickThrough_i = β₀ + β₁Mobile_i + U_i)?

How does the interpretation of the coefficient on the income to debt ratio change in the linear probability model of whether a mortgage applicant was approved for a loan or not if the outcome variable was Denied_i instead of Approved_i?

If one ran the regression on whether a mortgage applicant was approved for a loan or not (Approved_i) on their income to debt ratio, this would be an example of what sort of model?

If you are planning on running the regression model given by Tenure_i = β₀ + β₁Salary_i + β₂Years of Education_i + U_i, which of the following situations would cause this model to have a limited dependent variable?