2013-2014學(xué)年春季學(xué)期計(jì)量經(jīng)濟(jì)學(xué)基礎(chǔ)_期末試卷B

1、廈門(mén)大學(xué)計(jì)量經(jīng)濟(jì)學(xué)基礎(chǔ)課程試卷經(jīng)濟(jì)學(xué)院財(cái)政系2012級(jí)本科期末考試主考教師：王藝明 試卷類(lèi)型：（A卷/B卷）主考教師：王藝明 主考教師：王藝明 B卷1. The following equation was estimated using the data in CEOSAL1.RAW: (0.324) (0.033) (0.0129) (0.00026) n=209, =0.282This equation allows roe to have a diminishing effect on log(salary). Is this generality necessary? Explain

2、 why or why not.2. Using the data in GPA2.RAW, the following equation was estimated:sat _1(6.29) _1(3.83)hsize _(0.53)hsiz5(4.29)fe e(12.71)bla c(18.15)femaln=4,137, =0.0858.The variable sat is the combined SAT score, hsize is size of the students high school graduating class, in hundreds, female is

3、 a gender dummy variable, and black is a race dummy variable equal to one for blacks, and zero otherwise.(i) Is there strong evidence that should be included in the model? From this equation, what is the optimal high school size?(ii) Holding hsize fixed, what is the estimated difference in SAT score

4、 between nonblack females and nonblack males? How statistically significant is this estimated difference?(iii) What is the estimated difference in SAT score between nonblack males and black males? Test the null hypothesis that there is no difference between their scores, against the alternative that

5、 there is a difference.(iv) What is the estimated difference in SAT score between black females and nonblack females? What would you need to do to test whether the difference is statistically significant?3. An equation explaining chief executive officer salary is(0.30) (.032) (.004) (.089) (.085) (.

6、099) N=209, =0.357.The data used are in CEOSAL1.RAW, where finance, consprod, and utility are binary variables indicating the financial, consumer products, and utilities industries. The omitted industry is transportation.(i) Compute the approximate percentage difference in estimated salary between t

7、he utility and transportation industries, holding sales and roe fixed. Is the difference statistically significant at the 1% level?(ii) Use equation (7.10) to obtain the exact percentage difference in estimated salary between the utility and transportation industries and compare this with the answer

8、 obtained in part (i).(iii) What is the approximate percentage difference in estimated salary between the consumer products and finance industries? Write an equation that would allow you to test whether the difference is statistically significant.4. The variable smokes is a binary variable equal to

9、one if a person smokes, and zero otherwise. Using the data in SMOKE.RAW, we estimate a linear probability model for smokes:(0.855) (0.204) (0.026) (0.006) (0.006) 0.856 0.207 0.026 0.006 0.005(0.00006) (0.039) (0.052)0.00006 0.038 0.050n=807, =0.062.The variable white equals one if the respondent is

10、 white, and zero otherwise; cigpric= the per pack of cigarettes (in cents)income=annual incomeeduc= years of schoolingage=measured in yearsrestaurn= a binary indictor equal to unity if the person resides in a state with restaurant smoking restrictions.Both the usual and heteroskedasticityrobust stan

11、dard errors are reported.(i) Are there any important differences between the two sets of standard errors?(ii) Holding other factors fixed, if education increases by four years, what happens to the estimated probability of smoking?(iii) At what point does another year of age reduce the probability of

12、 smoking?(iv) Interpret the coefficient on the binary variable restaurn (a dummy variable equal to one if the person lives in a state with restaurant smoking restrictions).(v) Person number 206 in the data set has the following characteristics: Cigpric= 67.44, income =6,500, educ =16, age =77, resta

13、urn =0, white =0, and smokes=0. Compute the predicted probability of smoking for this person and comment on the result.5. Let denote the annual percentage change in gross domestic product and let denote a short-term interest rate. Suppose that is related to interest rates bywhere is uncorrelated wit

14、h , , and all other past values of interest rates. Suppose that the Federal Reserve follows the policy rule:where . (When last years GDP growth is above 3%, the Fed increases interest rates to prevent an “overheated” economy.) If is uncorrelated with all past values of and , argue that must be corre

15、lated with . (Hint: Lag the first equation for one time period and substitute for in the second equation.) Which Gauss-Markov assumption does this violate?6. Consider the following regression model:Note: Neither Y nor X assumes zero value.(i) Is this a linear regression model?(ii) How would you esti

16、mate this model?(iii) What is the behavior of Y as X tends to infinity?(iv) Can you give an example where such a model may be appropriate?7. From the data for 46 states in the United States for 1992, Baltagi obtained the following regression results:se = (0.91) (0.32) (0.20) = 0.27where C = cigarett

17、e consumption, packs per yearP = real price per packY = real disposable income per capita(i) What is the elasticity of demand for cigarettes with respect to price? Is it statistically significant? If so, is it statistically different from one?(ii) What is the income elasticity of demand for cigarett

18、es? Is it statistically significant? If not, what might be the reasons for it?(iii) How would you retrieve from the adjusted given above?8. Consider the following wage-determination equation for the British economy for the period 19501969: (1.129) (0.080) (0.072) (0.658)= 0.873 df = 15where W = wage

19、s and salaries per employeePF = prices of final output at factor costU = unemployment in Great Britain as a percentage of the total number of employees of Great Britaint = time(The figures in the parentheses are the estimated standard errors.)(i) Interpret the preceding equation.(ii) Are the estimat

20、ed coefficients individually significant?(iii)What is the rationale for the introduction of?(iv) Should the variable be dropped from the model? Why?(v) How would you compute the elasticity of wages and salaries per employee with respect to the unemployment rate U ?9.From data for 101 countries on pe

21、r capita income in dollars (X) and life expectancy in years (Y) in the early 1970s, Sen and Srivastava obtained the following regression results:se = (4.73) (0.859) (2.42) = 0.752where = 1 if 7, and= 0 otherwise. Note: When = 7, X = $1097 (approximately).(i) What might be the reason(s) for introduci

22、ng the income variable in the log form?(ii) How would you interpret the coefficient 9.39 of ?(iii) What might be the reason for introducing the regressor? How do you explain this regressor verbally? And how do you interpret the coefficient 3.36 of this regressor (Hint: linear piecewise regression)?(

23、iv) Assuming per capita income of $1097 as the dividing line between poorer and richer countries, how would you derive the regression for countries whose per capita is less than $1097 and the regression for countries whose per capita income is greater than $1097?(v) What general conclusions do you draw from the regression result presented in this proble