Question 1

Which of the following is not a problem with the linear probability model?
A)assumes constant marginal effects
B)generates predictions outside the (0,1)interval
C)heteroskedastic error term
D)coefficient estimates are biased

Accepted Answer

The linear probability model does not inherently produce biased coefficient estimates. The main issues with it include assuming constant marginal effects, generating predictions outside the (0,1) interval, and resulting in a heteroskedastic error term. Bias in coefficient estimates is not a characteristic problem of this model; it primarily concerns the model's assumptions and the nature of its predictions and error terms.

Question 2

For which type of model does the researcher have to assume independence of irrelevant alternatives?
A)probit
B)multinomial logit
C)linear probability
D)latent variable model

Accepted Answer

The independence of irrelevant alternatives assumption is necessary for the multinomial logit model. In this model, it is assumed that the odds of choosing any one alternative over another are not affected by the presence of a third alternative. This assumption is not necessary for probit, linear probability, or latent variable models.

Question 3

Which model requires the assumption of independence of irrelevant alternatives?
A)conditional logit
B)nested logit
C)multinomial probit
D)mixed logit

Accepted Answer

The conditional logit model requires the assumption of independence of irrelevant alternatives (IIA), which means the choice between any two alternatives is unaffected by the presence or characteristics of other alternatives.

Question 4

For what dependent variable should a Poisson regression model be considered?
A)satisfaction with job
B)GPA (grade point average)
C)number of mobile phones owned by a household
D)level of education completed

Accepted Answer

Poisson regression is typically used when the dependent variable represents counts or rates of occurrences, such as the number of mobile phones owned by a household. Satisfaction with job and level of education completed are not counts, and GPA is not necessarily a count or rate.

Question 5

Unobservable variables that enter into decisions are called
A)latent variables
B)endogenous variables
C)heterogeneity
D)count data

Accepted Answer

Latent variables are variables that are not directly observable but are inferred from observed variables. In decision making, there may be underlying factors or variables that influence the decision but cannot be measured directly. These variables are referred to as latent variables. Endogenous variables refer to variables that are affected by other variables in the model, while heterogeneity refers to differences in individual characteristics within a population. Count data refers to discrete numerical data that represents the number of times an event occurs.

Question 6

What is the difference between count data and ordered data?
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

Accepted Answer

Count data represent integer measurements (such as number of events) while ordered data represent a ranking or order (such as a scale of agreement or importance).

Question 7

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

Accepted Answer

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 censored. Censoring occurs when the true value of a variable is either not recorded or is recorded as being outside the range of possible observations. This can happen in many different situations, such as when a measurement instrument is not sensitive enough to record small values or when a study only includes participants who meet certain criteria. Truncated data occurs when measurements outside a certain range are excluded from the dataset, which is different from censoring. Qualitative and categorical data refer to variables that are not numerical in nature, such as gender or occupation.

Question 8

When should a probit model be used?
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

Accepted Answer

A probit model should be used when a binary choice model is needed that allows for varying marginal effects. The probit model estimates the probability of an event occurring and can also account for factors that affect the probability.

Question 9

When a decision maker has to choose between two mutually exclusive outcomes an econometrician may choose to use a(n)
A)binary choice model
B)ECM
C)random effects model
D)fixed effects model

Accepted Answer

A binary choice model is designed to analyze situations where there are only two possible outcomes, such as choosing between two mutually exclusive options. This type of model is often used in decision making contexts, making it the best choice for this scenario. ECM, random effects model, and fixed effects model are not relevant to this specific situation.

Question 10

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

Accepted Answer

The primary difference between ordered probit and ordered logit is the distributional assumption of the error term. Ordered probit assumes a normal distribution while ordered logit assumes a logistic distribution. Other differences, such as assumption of independence of irrelevant alternatives (A), varying variables by choice (C) and distribution of coefficient estimates (B) are not the primary distinguishing factors between the two models.

Question 11

When testing hypotheses of probit or logit coefficients,what 2 statistical tests are generally used? A)Wald and Likelihood Ratio B)z-test and t-test C)LM and Hausman D)F-test and R²

Accepted Answer

The Wald test and Likelihood Ratio test are generally used to test hypotheses of probit or logit coefficients. The Wald test is used to test whether individual coefficients are significantly different from zero, while the Likelihood Ratio test is used to compare the fit of two models.

Question 12

When should a Heckit model be used?
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

Accepted Answer

The Heckit model, or Heckman correction, is used when there is a sample selection bias, meaning the sample was not randomly selected. It corrects for this bias by modeling the selection process.

Question 13

How are logit and probit models different?
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

Accepted Answer

The main difference between logit and probit models is the choice of cumulative distribution function used to link the linear regression equation to the probability of the event occurring. Probit uses the cumulative density function (cdf) of the standard normal distribution, while logit uses the cdf of the logistic function.

Question 14

For which distribution is the mean equal to the variance?
A)standard normal
B)binomial
C)Poisson
D)chi-square

Accepted Answer

The mean and variance of a Poisson distribution are equal and are given by the parameter λ. Therefore, the answer is C.

Question 15

Which of the following statements is not true regarding a logit 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

Accepted Answer

The statement "it always generates larger marginal effects than a probit model" is not true. In fact, the sign and size of the marginal effects can vary based on the specific variables and context of the model, and there may be cases where a probit model generates larger marginal effects than a logit model.

Question 16

When is a Tobit model used?
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

Accepted Answer

Tobit models are used when the dependent variable has censoring or truncation. This typically occurs when values below or above a certain threshold cannot be observed or reported. Tobit models account for this censoring by estimating not only the probabilities of a certain outcome, but also the probabilities of censoring.

Question 17

When using least squares to estimate a model with a censored dependent variable,the results are
A)biased and inconsistent
B)unbiased,but not minimum variance
C)unbiased and consistent
D)biased but consistent

Accepted Answer

Least squares estimation in the presence of a censored dependent variable leads to biased and inconsistent estimates because the censored observations do not provide full information about the dependent variable, violating the assumptions necessary for the least squares method to produce unbiased and consistent estimates.

Question 18

What is the difference between multinomial logit and conditional logit?
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

Accepted Answer

Multinomial logit allows variables to vary by individual, while conditional logit allows variables to vary by both individual and choice.

Question 19

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

Accepted Answer

The Tobit model is used for censored data where some of the dependent variable values are not observed. It is a maximum likelihood approach that can estimate unbiased and consistent parameter estimates even with censored data. Least squares estimators, on the other hand, assume normality and can provide biased estimates when the dependent variable is censored. Therefore, using a Tobit model helps to address this issue and generate accurate estimates. Options B, C, and D are not necessarily accomplished by using a Tobit model.

Question 20

How is the average marginal effect calculated for a probit or logit model? 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 5^th and 95^th percentile

Accepted Answer

The average marginal effect in a probit or logit model is calculated by first computing the marginal effects for each observation in the sample and then taking the average of these marginal effects. This approach provides a summary measure of the effect of a unit change in the explanatory variable on the probability of the outcome, averaged across all observations.

Quiz 17: Qualitative and Limited Dependent Variable Models

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

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

Which model requires the assumption of independence of irrelevant alternatives?

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

Unobservable variables that enter into decisions are called

What is the difference between count data and ordered data?

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

When should a probit model be used?

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

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

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

When should a Heckit model be used?

How are logit and probit models different?

For which distribution is the mean equal to the variance?

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

When is a Tobit model used?

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

What is the difference between multinomial logit and conditional logit?

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

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