Chapter 9
1. Consider the following regression model: log(y) = β0 + β1x1 + β2x12 + β3x3 + u. This model will suffer from
functional form misspecification if _____.
a. β0 is omitted from the model
b. u is heteroskedastic
c. x12 is omitted from the model
d. x3 is a binary variable
2. A regression model suffers from functional form misspecification if _____.
a. a key variable is binary.
b. the dependent variable is binary.
c. an interaction term is omitted.
d. the coefficient of a key variable is zero.
3. Which of the following is true?
a. A functional form misspecification can occur if the level of a variable is used when the logarithm is
more appropriate.
b. A functional form misspecification occurs only if a key variable is uncorrelated with the error term. .
c. A functional form misspecification does not lead to biasedness in the ordinary least squares
estimators.
d. A functional form misspecification does not lead to inconsistency in the ordinary least squares
estimators.
4. Which of the following is true of Regression Specification Error Test (RESET)?
a. It tests if the functional form of a regression model is misspecified.
b. It detects the presence of dummy variables in a regression model.
c. It helps in the detection of heteroskedasticity when the functional form of the model is correctly
specified.
d. It helps in the detection of multicollinearity among the independent variables in a regression model.
5. A proxy variable _____.
a. increases the error variance of a regression model
b. cannot contain binary information
c. is used when data on a key independent variable is unavailable
d. is detected by running the Davidson-MacKinnon test
6. Which of the following assumptions is needed for the plug-in solution to the omitted variables
problem to provide consistent estimators?
a. The error term in the regression model exhibits heteroskedasticity.
b. The error term in the regression model is uncorrelated with all the independent variables.
c. The proxy variable is uncorrelated with the dependent variable.
d. The proxy variable has zero conditional mean.
7. Which of the following is a drawback of including proxy variables in a regression model?
a. It leads to misspecification analysis.
b. It reduces the error variance.
c. It increases the error variance.
d. It exacerbates multicollinearity.
8. Consider the following equation for household consumption expenditure:
Consmptn= β0+ β1Inc + β2Consmptn-1+ u
where ‘Consmptn’ measures the monthly consumption expenditure of a household, ‘Inc’ measures
household income and ‘Consmptn-1’ is the consumption expenditure in the previous month. Consmptn-1
is a _____ variable.
a. exogenous
b. binary variable
c. lagged dependent
d. proxy variable
9. A measurement error occurs in a regression model when _____.
a. the observed value of a variable used in the model differs from its actual value
b. the dependent variable is binary
c. the partial effect of an independent variable depends on unobserved factors
d. the model includes more than two independent variables
10. The classical errors-in-variables (CEV) assumption is that _____.
a. the error term in a regression model is correlated with all observed explanatory variables
b. the error term in a regression model is uncorrelated with all observed explanatory variables
c. the measurement error is correlated with the unobserved explanatory variable
d. the measurement error is uncorrelated with the unobserved explanatory variable
11. Which of the following is true of measurement error?
a. If measurement error in a dependent variable has zero mean, the ordinary least squares estimators
for the intercept are biased and inconsistent.
b. If measurement error in an independent variable is uncorrelated with the variable, the ordinary least
squares estimators are unbiased.
c. If measurement error in an independent variable is uncorrelated with other independent variables, all
estimators are biased.
d. If measurement error in a dependent variable is correlated with the independent variables, the
ordinary least squares estimators are unbiased.
12. Sample selection based on the dependent variable is called _____.
a. random sample selection
b. endogenous sample selection
c. exogenous sample selection
d. stratified sample selection
13. The method of data collection in which the population is divided into nonoverlapping, exhaustive
groups is called _____.
a. random sampling
b. stratified sampling
c. endogenous sampling
d. exogenous sampling
14. Which of the following types of sampling always causes bias or inconsistency in the ordinary least
squares estimators?
a. Random sampling
b. Exogenous sampling
c. Endogenous sampling
d. Stratified sampling
15. Which of the following is a difference between least absolute deviations (LAD) and ordinary least
squares (OLS) estimation?
a. OLS is more computationally intensive than LAD.
b. OLS is more sensitive to outlying observations than LAD.
c. OLS is justified for very large sample sizes while LAD is justified for smaller sample sizes.
d. OLS is designed to estimate the conditional median of the dependent variable while LAD is designed
to estimate the conditional mean.
16. An explanatory variable is called exogenous if it is correlated with the error term.
17. A multiple regression model suffers from functional form misspecification when it does not properly
account for the relationship between the dependent and the observed explanatory
variables.
18. The measurement error is the difference between the actual value of a variable and its reported
value.
19. Studentized residuals are obtained from the original OLS residuals by dividing them by an estimate
of their standard deviation.
20. The Least Absolute Deviations (LAD) estimators in a linear model minimize the sum of squared
residuals.