Question 1

What is the independent of irrelevant alternatives? Given an example.Why is it important? Explain.

Accepted Answer

The independent of irrelevant alternativ

Question 2

An estimator that is preferable to OLS in the case of binary dependent variables would be one that constrains all values of the dependent variable to

Accepted Answer

A) be negative. 
B) fall between 0 and 1. 
C) equal 0. 
D) equal 1. 
A) be negative. 
B) fall between 0 and 1. 
C) equal 0. 
D) equal 1.

Question 3

The coefficient estimates from the logit model are

Accepted Answer

A) not true marginal effects. 
B) true marginal effects. 
C) non-negative. 
D) biased. 
A) not true marginal effects. 
B) true marginal effects. 
C) non-negative. 
D) biased.

Question 4

Suppose you estimate a logit model explaining the probability of entering the labor force and you get the following estimated coefficients $Lab\widehat {or ~F}orce  _ { i } = 4.83 + 1.27 \text { Education } _ { i } - 3.06 \text { Female } _ { i } + 2.91 \text { Children Under 6} _ { i }$
You should conclude that

Accepted Answer

A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) men are estimated to be more likely than women to enter the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force. 
A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) men are estimated to be more likely than women to enter the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force.

Question 5

A binary dependent variable is one that takes on

Accepted Answer

A) continuous values. 
B) only the values 0 and 1. 
C) only integer values. 
D) only non-negative values. 
A) continuous values. 
B) only the values 0 and 1. 
C) only integer values. 
D) only non-negative values.

Question 6

Suppose you estimate a logit model explaining the probability of entering the labor force and you convert the estimated coefficients into the following estimated marginal effects $Lab\widehat {or ~F}orce  _ { i } = 1.67 + 0.42 \text { Education } _ { i } - 3.06 \text { Female } _ { i } + 2.91 \text { Children Under 6} _ { i }$
You should conclude that

Accepted Answer

A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) men are estimated to be more likely than women to enter the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force. 
A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) men are estimated to be more likely than women to enter the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force.

Question 7

When using the probit model,estimated marginal effects are obtained by

Accepted Answer

A) converting the estimated coefficients into marginal effects by plugging the values back into the multinomial logit function. 
B) estimating the model by OLS. 
C) estimating the model by the probit function. 
D) taking the derivative of the estimated coefficients. 
A) converting the estimated coefficients into marginal effects by plugging the values back into the multinomial logit function. 
B) estimating the model by OLS. 
C) estimating the model by the probit function. 
D) taking the derivative of the estimated coefficients.

Question 8

The ordered probit is more appropriate than the multinomial logit

Accepted Answer

A) when the dependent variable only taken on two values. 
B) when the independence of irrelevant alternatives is violated. 
C) always when dealing with categorical dependent variables. 
D) always when dealing with a non-multinomial dependent variable. 
A) when the dependent variable only taken on two values. 
B) when the independence of irrelevant alternatives is violated. 
C) always when dealing with categorical dependent variables. 
D) always when dealing with a non-multinomial dependent variable.

Question 9

What is a probit model? Why is it more appropriate than OLS? Explain.

Accepted Answer

The probit model is an alternative estim

Question 10

Suppose you are interested in explaining Student Evaluations of Teaching (which are calculated on the following 4-point scale: (1)Poor, (2)Fair, (3)Good,and (4)Excellent)in a sample of 15,239 college students and that using the multinomial logit you obtain the following coefficient estimates (standard errors in parentheses) $$\begin{array} { l l l lll } 
\widehat { S E T _ { i } } = &2.48 +& 0.97 \text { Avg. GPA } _ { i } - &1.52 \% \text { Freshmen } _ { i } - &0.24 \% \text { Male } _ { i } +& 2.04 \text { \% Majors } \

&( 0.83 ) &( 0.45 ) & ( 0.66 ) & ( 0.15 ) & ( 0.77 )

\end{array}$$ 
a)How should you interpret the estimated coefficient on Average GPA? Does this make economic sense? Explain.
b)How should you interpret the estimated coefficient on % Freshmen? Does this make economic sense? Explain.
c)Do these estimates provide all of the information that you desire? If not,how can you obtain that information? Explain.

Accepted Answer

Suppose you are interested in explaining

Question 11

Suppose you estimate a logit model explaining the probability of entering the labor force and you get the following estimated coefficients $Lab\widehat {or ~F}orce  _ { i } = 4.83 + 1.27 \text { Education } _ { i } - 3.06 \text { Female } _ { i } + 2.91 \text { Children Under 6} _ { i }$
You should conclude that

Accepted Answer

A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) that a higher education level increases the probability of being in the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force. 
A) men are estimated to be 306 percent more likely than women to enter the labor force. 
B) that a higher education level increases the probability of being in the labor force. 
C) each additional year of education increase the likelihood of entering the labor force by 483 percent. 
D) men are estimated to be 3.06 percent more likely than women to enter the labor force.

Question 12

The assumption required for the multinomial logit to be the most-preferred estimator for categorical dependent variables is referred to as

Accepted Answer

A) multinomial logit theorem. 
B) independence of relevant alternatives. 
C) independence of irrelevant alternatives. 
D) dependence of irrelevant alternatives. 
A) multinomial logit theorem. 
B) independence of relevant alternatives. 
C) independence of irrelevant alternatives. 
D) dependence of irrelevant alternatives.

Question 13

The coefficient estimates from the multinomial logit model

Accepted Answer

A) are constrained to be positive. 
B) must fall between 0 and 1. 
C) indicate the estimated marginal effects that the independent variables have on the dependent variable. 
D) indicate the general degree to which the independent variables are correlated with the dependent variable. 
A) are constrained to be positive. 
B) must fall between 0 and 1. 
C) indicate the estimated marginal effects that the independent variables have on the dependent variable. 
D) indicate the general degree to which the independent variables are correlated with the dependent variable.

Question 14

OLS is not the preferred model with binary dependent variables because it

Accepted Answer

A) does not account for negative values. 
B) only accounts for positive values. 
C) estimates constant marginal effects. 
D) does not account for values greater than 1. 
A) does not account for negative values. 
B) only accounts for positive values. 
C) estimates constant marginal effects. 
D) does not account for values greater than 1.

Question 15

The coefficient estimates from the multinomial logit model are

Accepted Answer

A) non-negative. 
B) not true marginal effects. 
C) true marginal effects. 
D) biased. 
A) non-negative. 
B) not true marginal effects. 
C) true marginal effects. 
D) biased.

Question 16

What is a logit model? Why is it more appropriate than OLS? Explain.

Accepted Answer

The logit model is an alternative estima

Question 17

The coefficient estimates from the logit model

Accepted Answer

A) indicate the estimated marginal effects that the independent variables have on the dependent variable. 
B) indicate the general degree to which the independent variables are correlated with the dependent variable. 
C) are constrained to be positive. 
D) must fall between 0 and 1. 
A) indicate the estimated marginal effects that the independent variables have on the dependent variable. 
B) indicate the general degree to which the independent variables are correlated with the dependent variable. 
C) are constrained to be positive. 
D) must fall between 0 and 1.

Question 18

The multinomial logit is preferable to OLS in the case of categorical dependent variables because it

Accepted Answer

A) is constrained to be non-negative. 
B) accounts for the categorical nature of the independent variables. 
C) accounts for the categorical nature of the dependent variable. 
D) unbiased. 
A) is constrained to be non-negative. 
B) accounts for the categorical nature of the independent variables. 
C) accounts for the categorical nature of the dependent variable. 
D) unbiased.

Question 19

When using the logit model,estimated marginal effects are obtained by

Accepted Answer

A) estimating the model by the logit function. 
B) estimating the model by OLS. 
C) taking the derivative of the estimated coefficients. 
D) converting the estimated coefficients into marginal effects by taking the derivative of the logit function with respect to the indepdendent variable. 
A) estimating the model by the logit function. 
B) estimating the model by OLS. 
C) taking the derivative of the estimated coefficients. 
D) converting the estimated coefficients into marginal effects by taking the derivative of the logit function with respect to the indepdendent variable.

Question 20

What do the coefficient estimates from the multinomial logit model represent? What do you need to do to determine true estimated marginal effects? Explain.

Accepted Answer

Coefficient estimates from the multinomi

What is the independent of irrelevant alternatives? Given an example.Why is it important? Explain.

An estimator that is preferable to OLS in the case of binary dependent variables would be one that constrains all values of the dependent variable to

The coefficient estimates from the logit model are

A binary dependent variable is one that takes on

When using the probit model,estimated marginal effects are obtained by

The ordered probit is more appropriate than the multinomial logit

What is a probit model? Why is it more appropriate than OLS? Explain.

The assumption required for the multinomial logit to be the most-preferred estimator for categorical dependent variables is referred to as

The coefficient estimates from the multinomial logit model

OLS is not the preferred model with binary dependent variables because it

The coefficient estimates from the multinomial logit model are

What is a logit model? Why is it more appropriate than OLS? Explain.

The coefficient estimates from the logit model

The multinomial logit is preferable to OLS in the case of categorical dependent variables because it

When using the logit model,estimated marginal effects are obtained by

What do the coefficient estimates from the multinomial logit model represent? What do you need to do to determine true estimated marginal effects? Explain.

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Auto-Correlation

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters

Exam 12: Limited Dependent Variables

What is the independent of irrelevant alternatives? Given an example.Why is it important? Explain.

An estimator that is preferable to OLS in the case of binary dependent variables would be one that constrains all values of the dependent variable to

The coefficient estimates from the logit model are

A binary dependent variable is one that takes on

When using the probit model,estimated marginal effects are obtained by

The ordered probit is more appropriate than the multinomial logit

What is a probit model? Why is it more appropriate than OLS? Explain.

The assumption required for the multinomial logit to be the most-preferred estimator for categorical dependent variables is referred to as

The coefficient estimates from the multinomial logit model

OLS is not the preferred model with binary dependent variables because it

The coefficient estimates from the multinomial logit model are

What is a logit model? Why is it more appropriate than OLS? Explain.

The coefficient estimates from the logit model

The multinomial logit is preferable to OLS in the case of categorical dependent variables because it

When using the logit model,estimated marginal effects are obtained by

What do the coefficient estimates from the multinomial logit model represent? What do you need to do to determine true estimated marginal effects? Explain.

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Auto-Correlation

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters