Chapter 17
1. Which of the following is an example of a binary response model?
a. MA model
b. ARCH model
c. GARCH model
d. Logit model
2. The model: G(z) = [exp(z)]/[1 + exp(z)],where G is between zero and one for all real numbers ‘z’,
represents a:
a. logit model.
b. probit model.
c. Tobit model.
d. linear probability model.
3. The model: G(z) = ∫ø(𝑣)𝑑𝑣
𝑧
−∞ where ø(z) = (2π)–1/2exp(-z2/2) represents a:
a. Tobit model.
b. logit model.
c. probit model.
d. linear probability model.
4. The likelihood ratio statistic is given by:
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)
5. The _____ model is designed to model corner solution dependent variables.
a. linear probability
b. logit
c. probit
d. Tobit
6. The model: y* = β0 + xβ + u, given u|x ~ Normal(0, σ2) and y = max(0, y*) represents a:
a. ARCH model.
b. GARCH model
c. Tobit model
d. logit model
7. Which of the following tests can be used to test hypotheses with multiple restrictions under a Tobit
model?
a. White test
b. Wald test
c. Dickey Fuller test
d. Durbin Watson test
8. A count variable refers to a dependent variable that can take on:
a. nonnegative integer values.
b. nonnegative fractional values.
c. negative fractional values.
d. negative integer values.
9. Which of the following statements is true?
a. Taking logarithmic of a count variable is a suitable way to model it.
b. All standard count data distributions exhibit heteroskedasticity.
c. The nonlinear least squares estimation aims at maximizing R2.
d. Count variables cannot take on the value zero.
10. The nominal distribution for count data is the:
a. binomial distribution.
b. normal distribution.
c. Poisson distribution.
d. Bernoulli distribution.
11. Which of the following statements is true?
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.
12. Which of the following statements is true?
a. OLS estimates in censored regression models are consistent estimators of the population coefficients.
b. In a truncated regression model, the samples are not included randomly from an underlying
population but are based on a given rule.
c. In a censored regression model, units in the sample are taken from a particular subset of the
population.
d. Maximum likelihood estimators are consistent in truncated regression models even if there is
nonnormality or heteroskedasticity in the error terms.
13. Duration is a variable that measures:
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.
14. Which of the following statements is true?
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.
15. Which of the following is a method to correct for sample selection bias for the problem of incidental
truncation?
a. Vector error correction method
b. First differencing method
c. Heckman’s method
d. Johansen method
16. The cumulative distribution function for a standard logistic random variable is a decreasing function.
17. The Tobit model relies crucially on normality and heteroskedasticity in the underlying latent variable
model.
18. In the Poisson regression model, the probability distribution is given by P(y = h|x) = exp[-
exp(xβ)][exp(xβ)]h/h!, h = 0, 1, …..
19. When a variable is top coded, its value is known only up to a certain threshold.
20. In case of endogenous sample selection, OLS is unbiased but consistent.