Question 1

The cumulative distribution function for a standard logistic random variable is a decreasing function.

Accepted Answer

A) True 
 B)False  False

Question 2

The Tobit model relies crucially on normality and heteroskedasticity in the underlying latent variable model.

Accepted Answer

A) True 
 B)False  False

Question 3

In the probit model, G is the:​

Accepted Answer

A) ​standard normal cumulative distribution function. 
B) ​cumulative distribution of normal distribution function. 
C)  ​standard normal probability distribution function. 
D)  ​normal probability distribution function. 
A) ​standard normal cumulative distribution function. 
B) ​cumulative distribution of normal distribution function. 
C)  ​standard normal probability distribution function. 
D)  ​normal probability distribution function. A

Question 4

A count variable refers to a dependent variable that can take on:

Accepted Answer

A) nonnegative integer values. 
B) nonnegative fractional values. 
C)  negative fractional values. 
D)  negative integer values. 
A) nonnegative integer values. 
B) nonnegative fractional values. 
C)  negative fractional values. 
D)  negative integer values.

Question 5

​The maximum likelihood estimates for Tobit models can be more easily obtained than the OLS estimates of a linear model.

Accepted Answer

A) True 
 B)False

Question 6

​A common form of sample selection that does not observe the dependent variable because of the outcome of another variable is called _____.

Accepted Answer

A) ​nonrandom sample selection 
B) ​exogenous sample selection 
C)  ​incidental truncation 
D)  ​endogenous sample selection 
A) ​nonrandom sample selection 
B) ​exogenous sample selection 
C)  ​incidental truncation 
D)  ​endogenous sample selection

Question 7

​The likelihood ratio statistic is nonnegative.

Accepted Answer

A) True 
 B)False

Question 8

The model: G(z) = [exp(z)]/[1 + exp(z)], where G is between zero and one for all real numbers 'z', represents a:

Accepted Answer

A) logit model. 
B) probit model. 
C)  Tobit model. 
D)  linear probability model. 
A) logit model. 
B) probit model. 
C)  Tobit model. 
D)  linear probability model.

Question 9

Which of the following statements is true?

Accepted Answer

A) A truncated regression is a special case of a random sample selection. 
B) Nonrandom sample selection can arise in cases of cross-sectional and time series data, but not in the case of panel data. 
C)  The Tobit regression model is based on endogenous sample selection. 
D)  The censored regression model is based on nonrandom sample selection. 
A) A truncated regression is a special case of a random sample selection. 
B) Nonrandom sample selection can arise in cases of cross-sectional and time series data, but not in the case of panel data. 
C)  The Tobit regression model is based on endogenous sample selection. 
D)  The censored regression model is based on nonrandom sample selection.

Question 10

Which of the following is a method to correct for sample selection bias for the problem of incidental truncation?

Accepted Answer

A) Vector error correction method 
B) First differencing method 
C)  Heckman's method 
D)  Johansen method 
A) Vector error correction method 
B) First differencing method 
C)  Heckman's method 
D)  Johansen method

Question 11

The nominal distribution for count data is the:

Accepted Answer

A) binomial distribution. 
B) normal distribution. 
C)  Poisson distribution. 
D)  Bernoulli distribution. 
A) binomial distribution. 
B) normal distribution. 
C)  Poisson distribution. 
D)  Bernoulli distribution.

Question 12

Which of the following tests can be used to test hypotheses with multiple restrictions under a Tobit model?

Accepted Answer

A) White test 
B) Wald test 
C)  Dickey Fuller test 
D)  Durbin Watson test 
A) White test 
B) Wald test 
C)  Dickey Fuller test 
D)  Durbin Watson test

Question 13

The model: G(z) =   , where   (z) = (2   )-1/2exp(-z2/2) represents a:

Accepted Answer

A) Tobit model. 
B) logit model. 
C)  probit model. 
D)  linear probability model. 
A) Tobit model. 
B) logit model. 
C)  probit model. 
D)  linear probability model.

Question 14

The _____ model is designed to model corner solution dependent variables.

Accepted Answer

A) linear probability 
B) logit 
C)  probit 
D)  Tobit 
A) linear probability 
B) logit 
C)  probit 
D)  Tobit

Question 15

​The difference between the LPM model and the logit and probit models is that:

Accepted Answer

A) ​the LPM assumes constant marginal effects for all the independent variables, while the logit and probit models imply diminishing magnitudes of the partial effects. 
B) ​the LPM assumes constant marginal effects for some of the independent variables, while the logit and probit models imply diminishing magnitudes of the partial effects. 
C)  ​the LPM assumes constant marginal effects for the dependent variable, while the logit and probit models imply diminishing magnitudes of the partial effects. 
D)  ​the LPM assumes different marginal effects for all independent variables, while the logit and probit models imply diminishing magnitudes of the marginal effects. 
A) ​the LPM assumes constant marginal effects for all the independent variables, while the logit and probit models imply diminishing magnitudes of the partial effects. 
B) ​the LPM assumes constant marginal effects for some of the independent variables, while the logit and probit models imply diminishing magnitudes of the partial effects. 
C)  ​the LPM assumes constant marginal effects for the dependent variable, while the logit and probit models imply diminishing magnitudes of the partial effects. 
D)  ​the LPM assumes different marginal effects for all independent variables, while the logit and probit models imply diminishing magnitudes of the marginal effects.

Question 16

Which of the following statements is true?

Accepted Answer

A) A probit or logit model should be used for corner solution outcomes, and a Poisson regression model should be used for a binary response. 
B) A Poisson regression model should be used for corner solution outcomes, and a probit or logit model should be used for a binary response. 
C)  A probit or logit model should be used for count variables, and a Poisson regression model should be used for a binary response. 
D)  A Poisson regression model should be used for count variables, and a probit or logit model should be used for a binary response. 
A) A probit or logit model should be used for corner solution outcomes, and a Poisson regression model should be used for a binary response. 
B) A Poisson regression model should be used for corner solution outcomes, and a probit or logit model should be used for a binary response. 
C)  A probit or logit model should be used for count variables, and a Poisson regression model should be used for a binary response. 
D)  A Poisson regression model should be used for count variables, and a probit or logit model should be used for a binary response.

Question 17

In case of endogenous sample selection, OLS is unbiased but consistent.

Accepted Answer

A) True 
 B)False

Question 18

In the Poisson regression model, the probability distribution is given by P(y = h|x) = exp[-exp(xâ)][exp(xâ)]h/h!, h = 0, 1, …..

Accepted Answer

A) True 
 B)False

Question 19

The likelihood ratio statistic is given by:

Accepted Answer

A) LR = (log-likelihoodunrestricted + log-likelihoodrestricted) 
B) LR = 2 × (log-likelihoodunrestricted + log-likelihoodrestricted) 
C)  LR = (log-likelihoodunrestricted - log-likelihoodrestricted) 
D)  LR = 2 × (log-likelihoodunrestricted - log-likelihoodrestricted) 
A) LR = (log-likelihoodunrestricted + log-likelihoodrestricted) 
B) LR = 2 × (log-likelihoodunrestricted + log-likelihoodrestricted) 
C)  LR = (log-likelihoodunrestricted - log-likelihoodrestricted) 
D)  LR = 2 × (log-likelihoodunrestricted - log-likelihoodrestricted)

Question 20

Duration is a variable that measures:

Accepted Answer

A) the time when a certain event occurs. 
B) the time before a certain event occurs. 
C)  the time after a certain event occurs. 
D)  the appropriate number of lags for a regression model. 
A) the time when a certain event occurs. 
B) the time before a certain event occurs. 
C)  the time after a certain event occurs. 
D)  the appropriate number of lags for a regression model.

The cumulative distribution function for a standard logistic random variable is a decreasing function.

The Tobit model relies crucially on normality and heteroskedasticity in the underlying latent variable model.

In the probit model, G is the:

A count variable refers to a dependent variable that can take on:

The maximum likelihood estimates for Tobit models can be more easily obtained than the OLS estimates of a linear model.

A common form of sample selection that does not observe the dependent variable because of the outcome of another variable is called _____.

The likelihood ratio statistic is nonnegative.

The model: G(z) = [exp(z)]/[1 + exp(z)], where G is between zero and one for all real numbers 'z', represents a:

Which of the following statements is true?

Which of the following is a method to correct for sample selection bias for the problem of incidental truncation?

The nominal distribution for count data is the:

Which of the following tests can be used to test hypotheses with multiple restrictions under a Tobit model?

The model: G(z) = , where (z) = (2 )^-1/2exp(-z²/2) represents a:

The _____ model is designed to model corner solution dependent variables.

The difference between the LPM model and the logit and probit models is that:

Which of the following statements is true?

In case of endogenous sample selection, OLS is unbiased but consistent.

In the Poisson regression model, the probability distribution is given by P(y = h|x) = exp[-exp(xâ)][exp(xâ)]h/h!, h = 0, 1, …..

The likelihood ratio statistic is given by:

Duration is a variable that measures:

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 17: Limited Dependent Variable Models and Sample Selection Corrections

The cumulative distribution function for a standard logistic random variable is a decreasing function.

The Tobit model relies crucially on normality and heteroskedasticity in the underlying latent variable model.

In the probit model, G is the:​

A count variable refers to a dependent variable that can take on:

​The maximum likelihood estimates for Tobit models can be more easily obtained than the OLS estimates of a linear model.

​A common form of sample selection that does not observe the dependent variable because of the outcome of another variable is called _____.

​The likelihood ratio statistic is nonnegative.

The model: G(z) = [exp(z)]/[1 + exp(z)], where G is between zero and one for all real numbers 'z', represents a:

Which of the following statements is true?

Which of the following is a method to correct for sample selection bias for the problem of incidental truncation?

The nominal distribution for count data is the:

Which of the following tests can be used to test hypotheses with multiple restrictions under a Tobit model?

The model: G(z) = , where (z) = (2 )-1/2exp(-z2/2) represents a:

The _____ model is designed to model corner solution dependent variables.

​The difference between the LPM model and the logit and probit models is that:

Which of the following statements is true?

In case of endogenous sample selection, OLS is unbiased but consistent.

In the Poisson regression model, the probability distribution is given by P(y = h|x) = exp[-exp(xâ)][exp(xâ)]h/h!, h = 0, 1, …..

The likelihood ratio statistic is given by:

Duration is a variable that measures:

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

In the probit model, G is the:

The maximum likelihood estimates for Tobit models can be more easily obtained than the OLS estimates of a linear model.

A common form of sample selection that does not observe the dependent variable because of the outcome of another variable is called _____.

The likelihood ratio statistic is nonnegative.

The model: G(z) = , where (z) = (2 )^-1/2exp(-z²/2) represents a:

The difference between the LPM model and the logit and probit models is that: