Question 1

In estimating a probit or logit model it is typical to maximize which objective function?

Accepted Answer

A)  T-statistic 
B)  Likelihood 
C)  Least squares 
D)  Adjusted R-squared 
A)  T-statistic 
B)  Likelihood 
C)  Least squares 
D)  Adjusted R-squared

Question 2

After estimating a probit model to determine the effect of salary offers on whether or not the offer is accepted; Yi* = β0 + β1SalaryOfferi + Ui, what would be the marginal effect associated with β1?

Accepted Answer

A)  The probability of accepting an offer 
B)  The probability of increasing the salary offered 
C)  The increase in the probability of an offer being accepted from a unit increase in the salary 
D)  The increase in salary associated with the offers that were accepted 
A)  The probability of accepting an offer 
B)  The probability of increasing the salary offered 
C)  The increase in the probability of an offer being accepted from a unit increase in the salary 
D)  The increase in salary associated with the offers that were accepted

Question 3

Why is a cumulative distribution function a natural choice for the modeling of a limited dependent variable-particularly a dichotomous one?

Accepted Answer

A)  Cumulative distribution functions are smooth. 
B)  Cumulative distribution functions are nonlinear. 
C)  Cumulative distribution functions are easy to calculate. 
D)  Cumulative distribution functions have a range between 0 and 1. 
A)  Cumulative distribution functions are smooth. 
B)  Cumulative distribution functions are nonlinear. 
C)  Cumulative distribution functions are easy to calculate. 
D)  Cumulative distribution functions have a range between 0 and 1.

Question 4

For probit/logit models the size of the marginal effect calculated will be a function of the estimated coefficient in addition to the:

Accepted Answer

A)  size of the independent variable change. 
B)  standard error on that coefficient estimate. 
C)  r-squared of the regression. 
D)  adjusted r-squared. 
A)  size of the independent variable change. 
B)  standard error on that coefficient estimate. 
C)  r-squared of the regression. 
D)  adjusted r-squared.

Question 5

When you estimate parameters by finding those that make the observed outcomes as likely as possible for a given model, you are using which method?

Accepted Answer

A)  Generalized method of moments 
B)  Maximum likelihood estimation 
C)  Instrumental variables 
D)  Least absolute deviations 
A)  Generalized method of moments 
B)  Maximum likelihood estimation 
C)  Instrumental variables 
D)  Least absolute deviations

Question 6

In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, which of the following conditions must hold from the resulting regression (ClickThroughi = β0 + β1TimeSpenti + Ui)?

Accepted Answer

A)  The sum of the residuals will be zero. 
B)  All the predictions will fall between 0 and 1. 
C)  The estimate for β1 will be negative. 
D)  The estimate for β0 will be positive. 
A)  The sum of the residuals will be zero. 
B)  All the predictions will fall between 0 and 1. 
C)  The estimate for β1 will be negative. 
D)  The estimate for β0 will be positive.

Question 7

If the regression results for a linear probability model of mortgage application are given by: Approvedi = 0.6(0.12) + -0.05(0.001)Debt2IncomeRatioi, with standard errors reported in parenthesis. How should we interpret the coefficient on the debt-to-income ratio variable?

Accepted Answer

A)  Increasing your debt to income ratio by 1 decreases your probability of being approved by 0.05. 
B)  Increasing your debt to income ratio by 1 decreases your probability of being approved by 0.55. 
C)  The probability of being approved for a mortgage is 0.05. 
D)  The probability of being approved for a mortgage is 0.6. 
A)  Increasing your debt to income ratio by 1 decreases your probability of being approved by 0.05. 
B)  Increasing your debt to income ratio by 1 decreases your probability of being approved by 0.55. 
C)  The probability of being approved for a mortgage is 0.05. 
D)  The probability of being approved for a mortgage is 0.6.

Question 8

What condition will fail to hold in a linear probability model?

Accepted Answer

A)  The determining function will be linear in parameters. 
B)  The moment conditions will define the estimate of the coefficients. 
C)  The errors will be homoscedastic. 
D)  The coefficient estimates will minimize the sum of squared residuals. 
A)  The determining function will be linear in parameters. 
B)  The moment conditions will define the estimate of the coefficients. 
C)  The errors will be homoscedastic. 
D)  The coefficient estimates will minimize the sum of squared residuals.

Question 9

If you were to estimate the linear probability model for whether or not a student passed their econometrics class and how that relates to if they are a business major (1 if yes, 0 if other), what will the coefficient on the dichotomous business major variable be?

Accepted Answer

A)  The econometrics class pass rate of business majors 
B)  The econometrics class pass rate of non-business majors 
C)  The average pass rate of all students 
D)  The difference in pass rates of business majors and non-business majors 
A)  The econometrics class pass rate of business majors 
B)  The econometrics class pass rate of non-business majors 
C)  The average pass rate of all students 
D)  The difference in pass rates of business majors and non-business majors

Question 10

After estimating a probit model for the likelihood of a website visitor clicking through conditional on the average income from the county in which the visit's IP address came from, you get the following results: ClickThroughi = -1.8(0.75) + 0.06(0.005)Incomei, where standard errors are reported in parenthesis. What would be the calculation that yields the marginal effect of income moving from $40,000 to $41,000 on the click-through rate?

Accepted Answer

A)  -1.8 + 0.06 = -1.74 
B)  Φ[0.06(41 - 40)] 
C)  Φ(-1.8 + 0.06 × 41) - Φ(-1.8 + 0.06 × 40) 
D)  0.8 
A)  -1.8 + 0.06 = -1.74 
B)  Φ[0.06(41 - 40)] 
C)  Φ(-1.8 + 0.06 × 41) - Φ(-1.8 + 0.06 × 40) 
D)  0.8

Question 11

After estimating a probit model for whether an individual purchases a particular product as a function of income, which of the following conditions must hold?

Accepted Answer

A)  The r-squared will be one. 
B)  The intercept will be positive. 
C)  The predictions will fall between zero and 1. 
D)  The predictions will sum to zero. 
A)  The r-squared will be one. 
B)  The intercept will be positive. 
C)  The predictions will fall between zero and 1. 
D)  The predictions will sum to zero.

Question 12

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

Accepted Answer

A)  is difficult to interpret the resulting coefficient estimates. 
B)  suffers from multicollinearity. 
C)  may generate predictions that fall outside of the range of the outcome variable. 
D)  may lead to homoscedastic errors. 
A)  is difficult to interpret the resulting coefficient estimates. 
B)  suffers from multicollinearity. 
C)  may generate predictions that fall outside of the range of the outcome variable. 
D)  may lead to homoscedastic errors.

Question 13

In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, the regression results are as follows: ClickThroughi = 0.4(0.08) + 0.07(0.01)TimeSpenti, where the standard errors of each coefficient are reported in parenthesis. Why would the fact that someone in the sample was on the website for 10 hours be problematic?

Accepted Answer

A)  It suggests the outcome is measured with error. 
B)  It suggests that you have a non-representative sample. 
C)  The prediction for that individual will fall above 1 (the maximum of ClickThrough). 
D)  It suggests that the errors are heteroscedastic. 
A)  It suggests the outcome is measured with error. 
B)  It suggests that you have a non-representative sample. 
C)  The prediction for that individual will fall above 1 (the maximum of ClickThrough). 
D)  It suggests that the errors are heteroscedastic.

Question 14

Which of the following is a merit of using the probit/logit model over the linear probability model for a limited dependent variable?

Accepted Answer

A)  By construction predictions from the model will fall within 0 and 1. 
B)  The marginal effects are easier to calculate. 
C)  The probit/logit model can be estimated using similar OLS. 
D)  The probit/logit model is better at finding causal estimates. 
A)  By construction predictions from the model will fall within 0 and 1. 
B)  The marginal effects are easier to calculate. 
C)  The probit/logit model can be estimated using similar OLS. 
D)  The probit/logit model is better at finding causal estimates.

Question 15

A limit-violating prediction is a predicted value that:

Accepted Answer

A)  only is normally distributed in the limit. 
B)  is heteroscedastic. 
C)  is measured with noise. 
D)  does not fall within that variables limits. 
A)  only is normally distributed in the limit. 
B)  is heteroscedastic. 
C)  is measured with noise. 
D)  does not fall within that variables limits.

Question 16

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

Accepted Answer

A)  Standard normal distribution 
B)  T-distribution 
C)  Logistic distribution 
D)  Exponential distribution 
A)  Standard normal distribution 
B)  T-distribution 
C)  Logistic distribution 
D)  Exponential distribution

Question 17

The primary difference between the probit and logit model is the:

Accepted Answer

A)  probit model uses a latent variable convention that the logit model does not. 
B)  probit model suffers from a heteroscedasticity problem while the logit does not. 
C)  choice of distribution for the unobservables. 
D)  probit model identifies causal effects, while the logit model identifies correlations. 
A)  probit model uses a latent variable convention that the logit model does not. 
B)  probit model suffers from a heteroscedasticity problem while the logit does not. 
C)  choice of distribution for the unobservables. 
D)  probit model identifies causal effects, while the logit model identifies correlations.

Question 18

If you are modeling shopping decisions at the grocery store, and construct an outcome variable that is coded as 0 for no soup purchases, 1 for soup purchased of only one brand, and 2 for soup purchase of multiple brands, it would be appropriate to model this outcome as limited dependent variable because the:

Accepted Answer

A)  outcome variable is ordinal. 
B)  outcome variable is cardinal. 
C)  range of possible values has consequential constraints. 
D)  variable is dichotomous. 
A)  outcome variable is ordinal. 
B)  outcome variable is cardinal. 
C)  range of possible values has consequential constraints. 
D)  variable is dichotomous.

Question 19

A dichotomous dependent variable is a variable that:

Accepted Answer

A)  leads to biased estimates of the coefficient of interest. 
B)  can take on just two values, typically recorded as 0 and 1. 
C)  requires that you use OLS to estimate any determining function. 
D)  cannot be used to identify causal effects. 
A)  leads to biased estimates of the coefficient of interest. 
B)  can take on just two values, typically recorded as 0 and 1. 
C)  requires that you use OLS to estimate any determining function. 
D)  cannot be used to identify causal effects.

Question 20

A linear probability model is regression analysis applied to what sort of variable?

Accepted Answer

A)  An instrumental variable 
B)  A variable found in a likelihood function 
C)  A dichotomous dependent variable 
D)  A fixed effect variable 
A)  An instrumental variable 
B)  A variable found in a likelihood function 
C)  A dichotomous dependent variable 
D)  A fixed effect variable

In estimating a probit or logit model it is typical to maximize which objective function?

After estimating a probit model to determine the effect of salary offers on whether or not the offer is accepted; Y_i^* = β₀ + β₁SalaryOffer_i + U_i, what would be the marginal effect associated with β₁?

Why is a cumulative distribution function a natural choice for the modeling of a limited dependent variable-particularly a dichotomous one?

For probit/logit models the size of the marginal effect calculated will be a function of the estimated coefficient in addition to the:

When you estimate parameters by finding those that make the observed outcomes as likely as possible for a given model, you are using which method?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, which of the following conditions must hold from the resulting regression (ClickThrough_i = β₀ + β₁TimeSpent_i + U_i)?

If the regression results for a linear probability model of mortgage application are given by: Approved_i = 0.6(0.12) + -0.05(0.001)Debt2IncomeRatio_i, with standard errors reported in parenthesis. How should we interpret the coefficient on the debt-to-income ratio variable?

What condition will fail to hold in a linear probability model?

If you were to estimate the linear probability model for whether or not a student passed their econometrics class and how that relates to if they are a business major (1 if yes, 0 if other), what will the coefficient on the dichotomous business major variable be?

After estimating a probit model for whether an individual purchases a particular product as a function of income, which of the following conditions must hold?

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

Which of the following is a merit of using the probit/logit model over the linear probability model for a limited dependent variable?

A limit-violating prediction is a predicted value that:

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

The primary difference between the probit and logit model is the:

A dichotomous dependent variable is a variable that:

A linear probability model is regression analysis applied to what sort of variable?

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

In estimating a probit or logit model it is typical to maximize which objective function?

After estimating a probit model to determine the effect of salary offers on whether or not the offer is accepted; Yi* = β0 + β1SalaryOfferi + Ui, what would be the marginal effect associated with β1?

Why is a cumulative distribution function a natural choice for the modeling of a limited dependent variable-particularly a dichotomous one?

For probit/logit models the size of the marginal effect calculated will be a function of the estimated coefficient in addition to the:

When you estimate parameters by finding those that make the observed outcomes as likely as possible for a given model, you are using which method?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, which of the following conditions must hold from the resulting regression (ClickThroughi = β0 + β1TimeSpenti + Ui)?

If the regression results for a linear probability model of mortgage application are given by: Approvedi = 0.6(0.12) + -0.05(0.001)Debt2IncomeRatioi, with standard errors reported in parenthesis. How should we interpret the coefficient on the debt-to-income ratio variable?

What condition will fail to hold in a linear probability model?

If you were to estimate the linear probability model for whether or not a student passed their econometrics class and how that relates to if they are a business major (1 if yes, 0 if other), what will the coefficient on the dichotomous business major variable be?

After estimating a probit model for whether an individual purchases a particular product as a function of income, which of the following conditions must hold?

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

Which of the following is a merit of using the probit/logit model over the linear probability model for a limited dependent variable?

A limit-violating prediction is a predicted value that:

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

The primary difference between the probit and logit model is the:

A dichotomous dependent variable is a variable that:

A linear probability model is regression analysis applied to what sort of variable?

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

After estimating a probit model to determine the effect of salary offers on whether or not the offer is accepted; Y_i^* = β₀ + β₁SalaryOffer_i + U_i, what would be the marginal effect associated with β₁?

In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, which of the following conditions must hold from the resulting regression (ClickThrough_i = β₀ + β₁TimeSpent_i + U_i)?

If the regression results for a linear probability model of mortgage application are given by: Approved_i = 0.6(0.12) + -0.05(0.001)Debt2IncomeRatio_i, with standard errors reported in parenthesis. How should we interpret the coefficient on the debt-to-income ratio variable?