Question 1

When should a probit model be used?

Accepted Answer

A) when you need a binary choice model that allows for varying marginal effects 
B) when you need to model the heteroskedasticity in the linear probability model 
C) to allow for endogenous regressors in a binary choice model 
D) to allow for multiple explanatory variables in a binary choice model 
A) when you need a binary choice model that allows for varying marginal effects 
B) when you need to model the heteroskedasticity in the linear probability model 
C) to allow for endogenous regressors in a binary choice model 
D) to allow for multiple explanatory variables in a binary choice model A

Question 2

Which of the following is not a problem with the linear probability model?

Accepted Answer

A) assumes constant marginal effects 
B) generates predictions outside the (0,1)interval 
C) heteroskedastic error term 
D) coefficient estimates are biased 
A) assumes constant marginal effects 
B) generates predictions outside the (0,1)interval 
C) heteroskedastic error term 
D) coefficient estimates are biased D

Question 3

What is the difference between multinomial logit and conditional logit?

Accepted Answer

A) conditional logit allows for variables to vary by individual and choice while multinomial logit only allows them to vary by individual 
B) multinomial logit allow for 3+ choices,conditional logit only accommodates 2 
C) multinomial logit is estimated with least squares,conditional logit by maximum likelihood 
D) multinomial logit has different coefficients for each choice equation,conditional logit has equal coefficients across choice equations on common variables 
A) conditional logit allows for variables to vary by individual and choice while multinomial logit only allows them to vary by individual 
B) multinomial logit allow for 3+ choices,conditional logit only accommodates 2 
C) multinomial logit is estimated with least squares,conditional logit by maximum likelihood 
D) multinomial logit has different coefficients for each choice equation,conditional logit has equal coefficients across choice equations on common variables A

Question 4

For what dependent variable should a Poisson regression model be considered?

Accepted Answer

A) satisfaction with job 
B) GPA (grade point average) 
C) number of mobile phones owned by a household 
D) level of education completed 
A) satisfaction with job 
B) GPA (grade point average) 
C) number of mobile phones owned by a household 
D) level of education completed

Question 5

Unobservable variables that enter into decisions are called

Accepted Answer

A) latent variables 
B) endogenous variables 
C) heterogeneity 
D) count data 
A) latent variables 
B) endogenous variables 
C) heterogeneity 
D) count data

Question 6

Which model requires the assumption of independence of irrelevant alternatives?

Accepted Answer

A) conditional logit 
B) nested logit 
C) multinomial probit 
D) mixed logit 
A) conditional logit 
B) nested logit 
C) multinomial probit 
D) mixed logit

Question 7

When a decision maker has to choose between two mutually exclusive outcomes an econometrician may choose to use a(n)

Accepted Answer

A) binary choice model 
B) ECM 
C) random effects model 
D) fixed effects model 
A) binary choice model 
B) ECM 
C) random effects model 
D) fixed effects model

Question 8

When using least squares to estimate a model with a censored dependent variable,the results are

Accepted Answer

A) biased and inconsistent 
B) unbiased,but not minimum variance 
C) unbiased and consistent 
D) biased but consistent 
A) biased and inconsistent 
B) unbiased,but not minimum variance 
C) unbiased and consistent 
D) biased but consistent

Question 9

When is a Tobit model used?

Accepted Answer

A) when you have censored data 
B) for categorical data with a normally distributed error term 
C) when you want to combine a probit and multinomial logit 
D) to estimate varying marginal effects in a binary choice model 
A) when you have censored data 
B) for categorical data with a normally distributed error term 
C) when you want to combine a probit and multinomial logit 
D) to estimate varying marginal effects in a binary choice model

Question 10

Which of the following statements is not true regarding a logit model?

Accepted Answer

A) it allows for marginal effects that vary with explanatory variables 
B) it is estimated by maximum likelihood 
C) it can be adjusted to accommodate more than 2 choice options 
D) it always generates larger marginal effects than a probit model 
A) it allows for marginal effects that vary with explanatory variables 
B) it is estimated by maximum likelihood 
C) it can be adjusted to accommodate more than 2 choice options 
D) it always generates larger marginal effects than a probit model

Question 11

When a substantial proportion of the observations for a dependent variable take on a limiting value,such as zero,the data are said to be

Accepted Answer

A) truncated 
B) qualitative 
C) categorical 
D) censored 
A) truncated 
B) qualitative 
C) categorical 
D) censored

Question 12

How is the average marginal effect calculated for a probit or logit model?

Accepted Answer

A) taking the marginal effect at the mean of the sample 
B) mean marginal effects are always .5 in a binary choice model 
C) calculate the marginal effects for each observation,then take the mean 
D) take the mean of the marginal effects at the 5th and 95th percentile 
A) taking the marginal effect at the mean of the sample 
B) mean marginal effects are always .5 in a binary choice model 
C) calculate the marginal effects for each observation,then take the mean 
D) take the mean of the marginal effects at the 5th and 95th percentile

Question 13

What is the difference between count data and ordered data?

Accepted Answer

A) there is no difference,they are two names for the same thing 
B) in count data the dependent variable is an integer that represent a measurement,in ordered data it represents a rank 
C) ordered data can be continuous while count data are discrete 
D) count data indicate ranking while ordered data are categorical 
A) there is no difference,they are two names for the same thing 
B) in count data the dependent variable is an integer that represent a measurement,in ordered data it represents a rank 
C) ordered data can be continuous while count data are discrete 
D) count data indicate ranking while ordered data are categorical

Question 14

What is accomplished by using a Tobit model rather than the least squares estimator?

Accepted Answer

A) it generate unbiased and consistent estimates on censored data 
B) it corrects sample selection bias 
C) it allows for choice models to be estimated using GLS 
D) it generates the same unbiased coefficient estimates,but with smaller variances 
A) it generate unbiased and consistent estimates on censored data 
B) it corrects sample selection bias 
C) it allows for choice models to be estimated using GLS 
D) it generates the same unbiased coefficient estimates,but with smaller variances

Question 15

When should a Heckit model be used?

Accepted Answer

A) when the sample was not randomly selected 
B) when the sample is small and error terms are not normally distributed 
C) as the alternative model to run a Hausman test on a Tobit model 
D) to minimize the variance of the error term when the sample selection process is suspect 
A) when the sample was not randomly selected 
B) when the sample is small and error terms are not normally distributed 
C) as the alternative model to run a Hausman test on a Tobit model 
D) to minimize the variance of the error term when the sample selection process is suspect

Question 16

When testing hypotheses of probit or logit coefficients,what 2 statistical tests are generally used?

Accepted Answer

A) Wald and Likelihood Ratio 
B) z-test and t-test 
C) LM and Hausman 
D) F-test and R2 
A) Wald and Likelihood Ratio 
B) z-test and t-test 
C) LM and Hausman 
D) F-test and R2

Question 17

What is the primary difference between ordered probit and ordered logit?

Accepted Answer

A) ordered logit requires assumption of independence of irrelevant alternatives,ordered probit does not 
B) they make different assumptions about the distribution of coefficient estimates 
C) ordered logit allows variables to vary by choice,ordered probit does not 
D) they make different assumptions about the distribution of the error term 
A) ordered logit requires assumption of independence of irrelevant alternatives,ordered probit does not 
B) they make different assumptions about the distribution of coefficient estimates 
C) ordered logit allows variables to vary by choice,ordered probit does not 
D) they make different assumptions about the distribution of the error term

Question 18

For which type of model does the researcher have to assume independence of irrelevant alternatives?

Accepted Answer

A) probit 
B) multinomial logit 
C) linear probability 
D) latent variable model 
A) probit 
B) multinomial logit 
C) linear probability 
D) latent variable model

Question 19

How are logit and probit models different?

Accepted Answer

A) probit is estimated by least squares,logit by maximum likelihood 
B) probit uses the cumulative density function (cdf)of the standard normal distribution and logit uses the cdf of the logistic function 
C) logit is for binary choice models and probit is used when there are two or more choices 
D) probit allows for endogenous regressors,logit does not 
A) probit is estimated by least squares,logit by maximum likelihood 
B) probit uses the cumulative density function (cdf)of the standard normal distribution and logit uses the cdf of the logistic function 
C) logit is for binary choice models and probit is used when there are two or more choices 
D) probit allows for endogenous regressors,logit does not

Question 20

For which distribution is the mean equal to the variance?

Accepted Answer

A) standard normal 
B) binomial 
C) Poisson 
D) chi-square 
A) standard normal 
B) binomial 
C) Poisson 
D) chi-square

When should a probit model be used?

Which of the following is not a problem with the linear probability model?

What is the difference between multinomial logit and conditional logit?

For what dependent variable should a Poisson regression model be considered?

Unobservable variables that enter into decisions are called

Which model requires the assumption of independence of irrelevant alternatives?

When a decision maker has to choose between two mutually exclusive outcomes an econometrician may choose to use a(n)

When using least squares to estimate a model with a censored dependent variable,the results are

When is a Tobit model used?

Which of the following statements is not true regarding a logit model?

When a substantial proportion of the observations for a dependent variable take on a limiting value,such as zero,the data are said to be

How is the average marginal effect calculated for a probit or logit model?

What is the difference between count data and ordered data?

What is accomplished by using a Tobit model rather than the least squares estimator?

When should a Heckit model be used?

When testing hypotheses of probit or logit coefficients,what 2 statistical tests are generally used?

What is the primary difference between ordered probit and ordered logit?

For which type of model does the researcher have to assume independence of irrelevant alternatives?

How are logit and probit models different?

For which distribution is the mean equal to the variance?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Filters

Exam 17: Qualitative and Limited Dependent Variable Models

When should a probit model be used?

Which of the following is not a problem with the linear probability model?

What is the difference between multinomial logit and conditional logit?

For what dependent variable should a Poisson regression model be considered?

Unobservable variables that enter into decisions are called

Which model requires the assumption of independence of irrelevant alternatives?

When a decision maker has to choose between two mutually exclusive outcomes an econometrician may choose to use a(n)

When using least squares to estimate a model with a censored dependent variable,the results are

When is a Tobit model used?

Which of the following statements is not true regarding a logit model?

When a substantial proportion of the observations for a dependent variable take on a limiting value,such as zero,the data are said to be

How is the average marginal effect calculated for a probit or logit model?

What is the difference between count data and ordered data?

What is accomplished by using a Tobit model rather than the least squares estimator?

When should a Heckit model be used?

When testing hypotheses of probit or logit coefficients,what 2 statistical tests are generally used?

What is the primary difference between ordered probit and ordered logit?

For which type of model does the researcher have to assume independence of irrelevant alternatives?

How are logit and probit models different?

For which distribution is the mean equal to the variance?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Filters