Complete Test Bank Ch.2 The Simple Linear Regression Model - Principles of Econometrics 5e Complete Test Bank by R. Carter Hill. DOCX document preview.
File: Ch02, Chapter 2, The Simple Linear Regression Model
Multiple Choice
1. Consider the probability density function of a random variable that is normally distributed. The center of this distribution is called the _____ and the measure of the dispersion of this random variable is called the _____.
a. sample mean; sample variance
b. conditional mean; conditional variance
c. sample variance; sample mean
d. conditional variance; conditional mean
2. In an economic model that uses income to predict monthly expenditures on entertainment, _____ would be the dependent variable.
a. income
b. monthly expenditures on entertainment
c. income elasticity
d. demand for entertainment
3. In an economic model that uses income to predict monthly expenditures on entertainment, _____ would be the independent or explanatory variable.
a. income
b. monthly expenditures on entertainment
c. income elasticity
d. demand for entertainment
4. If a randomly selected data pair 
for 
are assumed to follow the same joint probability density function, then we say that the data pairs are _____.
a. random variables
b. statistical inferences
c. identically distributed
d. statistically independent
5. Let 
be a random error term and 
be an independent random variable. If we can say that 
is strictly exogenous, then which of the following must hold?
a. 
b. 
c. 
d. 
6. If the conditional variation of the random errors is not constant (i.e., 
), then we say that random errors are _____.
a. homoscedastic
b. exogenous
c. heteroskedastic
d. serially correlated
7. The OLS estimators for 
and 
are formulas derived by minimizing _____.
a. the sum of the error terms or residuals
b. the sum of the squared residuals
c. the slope of the regression line
d. the fit of the regression line to the observed data
8. Suppose you have the following estimated regression equation: 
. What would the forecast value be when the independent variable is 15.00?
a. 196.76
b. 16.30
c. 244.50
d. 32.19
9. Suppose you have estimated a simple linear demand equation for single-game tickets for a minor league baseball team. The estimated equation is 
where 
is the number of tickets sold measured in thousands and 
is the dollar price of a ticket. Further assume that the point of the means is 
. Given this information and everything else held constant, the estimate of the price elasticity of demand for tickets is _____.
a. -0.16
b. 1.64
c. 0.16
d. -1.64
10. In the OLS model, what happens to 
as the sample size,
, increases?
a. it also increases
b. it decreases
c. it does not change
d. cannot be determined without more information
11. If 
is an estimator for
such that 
, then it must be the case that 
is _____.
a. an efficient estimator
b. an unbiased estimator
c. a linear estimator
d. a preferred estimator
12. Under the Gauss-Markov Theorem when the first five assumptions of the Simple Linear Regression Model are met, what estimators of 
and 
may have smaller variances than 
and 
?
a. none
b. a non-linear estimator
c. a normally distributed estimator
d. an estimator derived from economic theory
13. If we use 
as an estimator of 2 it is _____, but it can be corrected by _____.
a. biased; changing the numerator to 
b. non-linear; changing the denominator to N – 2
c. biased; changing the denominator to N-2
d. non-linear; taking the log of each term
14. Which of the following non-linear adjustments CANNOT be accommodated using OLS?
a. Including an independent variable that has been raised to a power.
b. Taking a logarithmic transformation of the dependent variable.
c. Including a binary indicator variable.
d. Raising parameters to a power.
15. Suppose you have the equation 
where INCOME is annual household income (in thousands) and ENT_EXP is annual entertainment expenses. How do you interpret the estimated value of 
?
a. The income elasticity of entertainment.
b. When multiplied by 100, it is the percentage increase in entertainment expenses associated with an additional $1000 in income.
c. The increase in entertainment expenses associated with a 1% increase in income.
d. The average of the logarithm of entertainment expenses for a household with zero income.
16. Suppose you have the equation where INCOME is annual household income (in thousands) and ENT_EXP is annual entertainment expenses. How do you interpret the estimated value of 
?
a. The income elasticity of entertainment.
b. When multiplied by 100, it is the percentage increase in entertainment expenses associated with an additional $1000 in income.
c. The increase in entertainment expenses associated with a 1% increase in income.
d. The average of the logarithm of entertainment expenses for a household with zero income.
17. Suppose you have estimated the equation 
where 
is annual income in thousands and MALE is an indicator variable such that it takes the value of 1 for males and the value of 0 for females. According to this model, the average income for females is _____.
a. $33,750
b. $35,200
c. $32,300
d. undetermined
18. Suppose you have estimated the equation where is annual income in thousands and MALE is an indicator variable such that it takes the value of 1 for males and the value of 0 for females. According to this model, the average income for males is _____.
a. $33,750
b. $35,200
c. $32,300
d. undetermined
19. Suppose you obtain N randomly selected data pairs from a population. Let 
denote the 
pair, i = 1, …, N. Briefly explain what each of the following terms mean.
a. random variable
b. statistically independent
c. identically distributed
d. random sample
20. Briefly summarize the six assumptions of the Simple Linear Regression Model.
1. Econometric Model: All data pairs 
collected from a population satisfy the relationship
, i = 1, …, N.
2. Strict Exogeneity: The conditional expected value of the random error, 
, is zero. That is, 
where
.
3. Conditional Homoscedasticity: conditional variance of the error is constant. That is, 
.
4. Conditionally Uncorrelated Errors: the conditional covariance of random errors is zero. That is, 
for 
.
5. Explanatory Variables Must Vary: 
must take at least two different values.
6. (Optional) Error Normality: conditional distribution of the random error is normally distributed. That is, 
.
Level: Easy – Knowledge
AACSB: Reflective Thinking
Section 2.2