Question 1

Maximum Likelihood Estimation (MLE) uses t-tests, just like OLS.

Accepted Answer

A) True 
 B)False  False

Question 2

A dichotomous dependent variable signifies that an event either happened or did not.

Accepted Answer

A) True 
 B)False  True

Question 3

List and explain the benefits and drawbacks of employing a linear probability model.

Accepted Answer

The LPM model may mischaracterize the relationship between X and Y by assuming that the effects of a variable on the probability of a 1 is constant across values of X Furthermore, the fitted values (Yhat may be greater than 1 or less than 0, which does not make sense since probabilities can only take on values between 0 and 1 A benefit of LPM is that the coefficient estimates are easy to interpret, where a 1 unit increase in X1 is associated with a B1 increase in the probability that Y is equal to 1

Question 4

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a LPM model?

Accepted Answer

A)  Calculate the difference in fitted values when the variable is at its actual value and increased by a standard deviation, holding all other variables at their actual values. 
B)  Standardize each observation by dividing each observation by the standard deviation. 
C)  The coefficient indicates how much a one unit increase in X1 changes the predicted probability. 
D)  Use a latent variable. 
A)  Calculate the difference in fitted values when the variable is at its actual value and increased by a standard deviation, holding all other variables at their actual values. 
B)  Standardize each observation by dividing each observation by the standard deviation. 
C)  The coefficient indicates how much a one unit increase in X1 changes the predicted probability. 
D)  Use a latent variable.

Question 5

Which of the following is not a property of MLE if there is no endogeneity?

Accepted Answer

A)  Parameters are normally distributed. 
B)  Parameters are consistent. 
C)  Fitted values can be produced. 
D)  Coefficients minimize the sum of squared residuals. 
A)  Parameters are normally distributed. 
B)  Parameters are consistent. 
C)  Fitted values can be produced. 
D)  Coefficients minimize the sum of squared residuals.

Question 6

In a probit model, the interpretation of the estimated effect of X1 on the probability Y=1 depends on:

Accepted Answer

A)  The current level of X1 
B)  The current level of the other independent variables. 
C)  The current level of Y. 
D)  Both a and b 
A)  The current level of X1 
B)  The current level of the other independent variables. 
C)  The current level of Y. 
D)  Both a and b

Question 7

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a probit model?

Accepted Answer

A)  Calculate the difference in fitted values when the variable is at its actual value and increased by a standard deviation, holding all other variables at their actual values. 
B)  Standardize each observation by dividing each observation by the standard deviation. 
C)  The coefficient indicates how much a one unit increase in X1 changes the predicted probability. 
D)  Use a latent variable. 
A)  Calculate the difference in fitted values when the variable is at its actual value and increased by a standard deviation, holding all other variables at their actual values. 
B)  Standardize each observation by dividing each observation by the standard deviation. 
C)  The coefficient indicates how much a one unit increase in X1 changes the predicted probability. 
D)  Use a latent variable.

Question 8

Write down the equation for a probit model with one independent variable.

Accepted Answer

The answer of Write down the equation for a probit...

Question 9

Which of the following is false?

Accepted Answer

A)  Fitted values from probit models and logit models are very similar when they are based on the same data. 
B)  The coefficients in a probit and logit model are very similar when they are based on the same data. 
C)  Probit models are slightly more accurate. 
D)  Logit models make sense when using logged variables. 
A)  Fitted values from probit models and logit models are very similar when they are based on the same data. 
B)  The coefficients in a probit and logit model are very similar when they are based on the same data. 
C)  Probit models are slightly more accurate. 
D)  Logit models make sense when using logged variables.

Question 10

We can use OLS to estimate a LPM model.

Accepted Answer

A) True 
 B)False

Question 11

Probit and logit coefficients are interpreted the same way as LPM coefficients.

Accepted Answer

A) True 
 B)False

Question 12

The cumulative distribution function

Accepted Answer

A)  Tells us how much of a normal distribution is to the right of any given point. 
B)  Tell us how much of a normal distribution is to the left of any given point. 
C)  Shows the probability for each possible value of the random variable. 
D)  Has the same shape as a normal distribution, but wider tails. 
A)  Tells us how much of a normal distribution is to the right of any given point. 
B)  Tell us how much of a normal distribution is to the left of any given point. 
C)  Shows the probability for each possible value of the random variable. 
D)  Has the same shape as a normal distribution, but wider tails.

Question 13

Write down the equation for a logit model with one independent variable.

Accepted Answer

The answer of Write down the equation for a logit...

Question 14

Both OLS and probit models require that the error term be normally distributed.

Accepted Answer

A) True 
 B)False

Question 15

In order to run a hypothesis test on multiple coefficients to check if they are different from one another (equal, bigger or smaller) in a probit model, we:

Accepted Answer

A)  Use an F Test 
B)  Use a likelihood ratio test 
C)  Run multiple t-tests 
D)  Use a Chi-squared test 
A)  Use an F Test 
B)  Use a likelihood ratio test 
C)  Run multiple t-tests 
D)  Use a Chi-squared test

Question 16

Which of the following is a drawback of using LPM models?

Accepted Answer

A)  LPM models cannot handle dichotomous dependent variables. 
B)  LPM fitted values do not always fall within the range of 0 and 1. 
C)  LPM models require us to assume errors are normally distributed. 
D)  LPM models are complicated to interpret. 
A)  LPM models cannot handle dichotomous dependent variables. 
B)  LPM fitted values do not always fall within the range of 0 and 1. 
C)  LPM models require us to assume errors are normally distributed. 
D)  LPM models are complicated to interpret.

Question 17

Which of the following is a characteristic of latent variables?

Accepted Answer

A)  The value of the latent variable is high, than the dependent variable for that observation is likely to be 0. 
B)  The value of the latent variable is high, than the dependent variable for that observation is likely to be 1. 
C)  It is an observed continuous variable reflecting the propensity of an individual observation of Yi to be equal to 0 or 1. 
D)  They are normally distributed. 
A)  The value of the latent variable is high, than the dependent variable for that observation is likely to be 0. 
B)  The value of the latent variable is high, than the dependent variable for that observation is likely to be 1. 
C)  It is an observed continuous variable reflecting the propensity of an individual observation of Yi to be equal to 0 or 1. 
D)  They are normally distributed.

Question 18

Explain how we can interpret probit coefficients using the observed-value, discrete differences method in the case where X1 is continuous.

Accepted Answer

Calculate fitted value and the

Question 19

Explain the logic behind the use of latent variables in order to explain observed variables.

Accepted Answer

A latent variable is something that is n

Question 20

Which of the following is the equation for a likelihood ratio test?

Accepted Answer

A)  LR=(logLur - logLr) 
B)  LR=2(logLur - logLr) 
C)  LR=(logLr - logLur) 
D)  LR=2(logLr - logLur) 
A)  LR=(logLur - logLr) 
B)  LR=2(logLur - logLr) 
C)  LR=(logLr - logLur) 
D)  LR=2(logLr - logLur)

Maximum Likelihood Estimation (MLE) uses t-tests, just like OLS.

A dichotomous dependent variable signifies that an event either happened or did not.

List and explain the benefits and drawbacks of employing a linear probability model.

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a LPM model?

Which of the following is not a property of MLE if there is no endogeneity?

In a probit model, the interpretation of the estimated effect of X1 on the probability Y=1 depends on:

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a probit model?

Write down the equation for a probit model with one independent variable.

Which of the following is false?

We can use OLS to estimate a LPM model.

Probit and logit coefficients are interpreted the same way as LPM coefficients.

The cumulative distribution function

Write down the equation for a logit model with one independent variable.

Both OLS and probit models require that the error term be normally distributed.

In order to run a hypothesis test on multiple coefficients to check if they are different from one another (equal, bigger or smaller) in a probit model, we:

Which of the following is a drawback of using LPM models?

Which of the following is a characteristic of latent variables?

Explain how we can interpret probit coefficients using the observed-value, discrete differences method in the case where X1 is continuous.

Explain the logic behind the use of latent variables in order to explain observed variables.

Which of the following is the equation for a likelihood ratio test?

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

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

Exam 12: Dummy Dependent Variables

Maximum Likelihood Estimation (MLE) uses t-tests, just like OLS.

A dichotomous dependent variable signifies that an event either happened or did not.

List and explain the benefits and drawbacks of employing a linear probability model.

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a LPM model?

Which of the following is not a property of MLE if there is no endogeneity?

In a probit model, the interpretation of the estimated effect of X1 on the probability Y=1 depends on:

Which of the following is an appropriate way to interpret a coefficient on a continuous independent variable (X1) from a probit model?

Write down the equation for a probit model with one independent variable.

Which of the following is false?

We can use OLS to estimate a LPM model.

Probit and logit coefficients are interpreted the same way as LPM coefficients.

The cumulative distribution function

Write down the equation for a logit model with one independent variable.

Both OLS and probit models require that the error term be normally distributed.

In order to run a hypothesis test on multiple coefficients to check if they are different from one another (equal, bigger or smaller) in a probit model, we:

Which of the following is a drawback of using LPM models?

Which of the following is a characteristic of latent variables?

Explain how we can interpret probit coefficients using the observed-value, discrete differences method in the case where X1 is continuous.

Explain the logic behind the use of latent variables in order to explain observed variables.

Which of the following is the equation for a likelihood ratio test?

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

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters