经济统计计量模型Chapter-2.docx

Chapter 2

1. A dependent variable is also known as a(n) _____.
a. explanatory variable
b. control variable
c. predictor variable
d. response variable

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: A dependent variable is known as a response variable.

2. If a change in variable x causes a change in variable y, variable x is called the _____.
a. dependent variable
b. explained variable
c. explanatory variable
d. response variable

Answer: c
Difficulty: Easy
Bloom’s: Comprehension
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: If a change in variable x causes a change in variable y, variable x is called the independent variable or the explanatory variable.

3. In the equation y = β0 + β1x + u, β0 is the _____.
a. dependent variable
b. independent variable
c. slope parameter
d. intercept parameter

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: In the equation y = β0 + β1x + u, β0 is the intercept parameter.

4. In the equation y = β0 + β1x + u, what is the estimated value of β0?
a. y- β1 x
b. y+β1x
c. i=1n(xi-x)(yi-y)i=1n(xi)2
d. i=1nxy

Answer: a
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:
Feedback: The estimated value of β0 is y- β1 x.

5. In the equation c = β0 + β1i + u, c denotes consumption and i denotes income. What is the residual for the 5th observation if c5=$500 and c5=$475?
a. $975
b. $300
c. $25
d. $50

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:
Feedback: The formula for calculating the residual for the ith observation is ui=yi-yi. In this case, the residual is u5=c5-c5 =$500 -$475= $25.

6. What does the equation y=β0+β1x denote if the regression equation is y = β0 + β1x1 + u?
a. The explained sum of squares
b. The total sum of squares
c. The sample regression function
d. The population regression function

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Deriving the Ordinary Least Squares Estimates
BUSPROG:
Feedback: The equation y=β0+β1x denotes the sample regression function of the given regression model.

7. Consider the following regression model: y = β0 + β1x1 + u. Which of the following is a property of Ordinary Least Square (OLS) estimates of this model and their associated statistics?
a. The sum, and therefore the sample average of the OLS residuals, is positive.
b. The sum of the OLS residuals is negative.
c. The sample covariance between the regressors and the OLS residuals is positive.
d. The point (x, y) always lies on the OLS regression line.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: An important property of the OLS estimates is that the point (x, y) always lies on the OLS regression line. In other words, if x=x, the predicted value of y is y.

8. The explained sum of squares for the regression function, yi=β0+β1x1+u1, is defined as _____.
a. i=1n(yi-y)2
b. i=1n(yi-y)2
c. i=1 nui
d.i=1n(ui)2

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: The explained sum of squares is defined as i=1n(yi-y)2

9. If the total sum of squares (SST) in a regression equation is 81, and the residual sum of squares (SSR) is 25, what is the explained sum of squares (SSE)?
a. 64
b. 56
c. 32
d. 18

Answer: b
Difficulty: Moderate
Bloom’s: Application
A-Head: Properties of OLS on Any Sample of Data
BUSPROG: Analytic
Feedback: Total sum of squares (SST) is given by the sum of explained sum of squares (SSE) and residual sum of squares (SSR). Therefore, in this case, SSE=81-25=56.

10. If the residual sum of squares (SSR) in a regression analysis is 66 and the total sum of squares (SST) is equal to 90, what is the value of the coefficient of determination?
a. 0.73
b. 0.55
c. 0.27
d. 1.2

Answer: c
Difficulty: Moderate
Bloom’s: Application
A-Head: Properties of OLS on Any Sample of Data
BUSPROG: Analytic
Feedback: The formula for calculating the coefficient of determination is R2=1-SSRSST . In this case, R2=1- 6690=0.27

11. Which of the following is a nonlinear regression model?
a. y = β0 + β1x1/2 + u
b. log y = β0 + β1log x +u
c. y = 1 / (β0 + β1x) + u
d. y = β0 + β1x + u

Answer: c
Difficulty: Moderate
Bloom’s: Comprehension
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: A regression model is nonlinear if the equation is nonlinear in the parameters. In this case, y=1 / (β0 + β1x) + u is nonlinear as it is nonlinear in its parameters.

12. Which of the following is assumed for establishing the unbiasedness of Ordinary Least Square (OLS) estimates?
a. The error term has an expected value of 1 given any value of the explanatory variable.
b. The regression equation is linear in the explained and explanatory variables.
c. The sample outcomes on the explanatory variable are all the same value.
d. The error term has the same variance given any value of the explanatory variable.

Answer: d
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The error u has the same variance given any value of the explanatory variable.

13. The error term in a regression equation is said to exhibit homoskedasticty if _____.
a. it has zero conditional mean
b. it has the same variance for all values of the explanatory variable.
c. it has the same value for all values of the explanatory variable
d. if the error term has a value of one given any value of the explanatory variable.

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The error term in a regression equation is said to exhibit homoskedasticty if it has the same variance for all values of the explanatory variable.

14. In the regression of y on x, the error term exhibits heteroskedasticity if _____.
a. it has a constant variance
b. Var(y|x) is a function of x
c. x is a function of y
d. y is a function of x

Answer: b
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: Heteroskedasticity is present whenever Var(y|x) is a function of x because Var(u|x) = Var(y|x).

15. What is the estimated value of the slope parameter when the regression equation, y = β0 + β1x1 + u passes through the origin?
a.i=1nyi
b.i=1n(yi-y)
c. i=1nxiyii=1nxi2
d. i=1n(yi-y)2

Answer: c
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Regression through the Origin and Regression on a Constant
BUSPROG:
Feedback: The estimated value of the slope parameter when the regression equation passes through the origin is i=1nxiyii=1nxi2 .

16. A natural measure of the association between two random variables is the correlation coefficient.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Definition of the Simple Regression Model
BUSPROG:
Feedback: A natural measure of the association between two random variables is the correlation coefficient.

17. The sample covariance between the regressors and the Ordinary Least Square (OLS) residuals is always positive.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: The sample covariance between the regressors and the Ordinary Least Square (OLS) residuals is zero.

18. R2 is the ratio of the explained variation compared to the total variation.

Answer: True
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Properties of OLS on Any Sample of Data
BUSPROG:
Feedback: The sample covariance between the regressors and the Ordinary Least Square (OLS) residuals is zero.

19. There are n-1 degrees of freedom in Ordinary Least Square residuals.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: There are n-2 degrees of freedom in Ordinary Least Square residuals.

20. The variance of the slope estimator increases as the error variance decreases.

Answer: False
Difficulty: Easy
Bloom’s: Knowledge
A-Head: Expected Values and Variances of the OLS Estimators
BUSPROG:
Feedback: The variance of the slope estimator increases as the error variance increases.

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