1、廈門大學計量經濟學基礎課程試卷經濟學院財政系2012級本科期末考試主考教師：王藝明 試卷類型：（A卷/B卷）主考教師：王藝明 主考教師：王藝明 A卷1.Using the data in RDCHEM.RAW, the following equation was obtained by OLS：rdinten (0.429) _ (0.00014) sale (0.0000000037) sales2n=32, =0.1484.(i) At what point does the marginal effect of sales on rdintens become negative?(ii

2、) Would you keep the quadratic term in the model? Explain.(iii) Define salesbil as sales measured in billions of dollars: salesbil=sales/1,000. Rewrite the estimated equation with salesbil and as the independent variables. Be sure to report standard errors and the R-squared. Hint: Note that = /.2. U

3、sing the data in SLEEP75.RAW (see also Problem 3.3), we obtain the estimated equation(235.11) (.018) (5.86) (11.21) (.134) (34.33)n=706, =0.123, =0.117.The variable sleep is total minutes per week spent sleeping at night, totwrk is total weekly minutes spent working, educ and age are measured in yea

4、rs, and male is a gender dummy.(i) All other factors being equal, is there evidence that men sleep more than women? How strong is the evidence?(ii) Is there a statistically significant tradeoff between working and sleeping? What is the estimated tradeoff?(iii) What other regression do you need to ru

5、n to test the null hypothesis that, holding other factors fixed, age has no effect on sleeping?3. Consider a linear model to explain monthly beer consumption:Write the transformed equation that has a homoskedastic error term.4. Decide if you agree or disagree with each of the following statements an

6、d give a brief explanation of your decision:(i) Like cross-sectional observations, we can assume that most time series observations are independently distributed.(ii) The OLS estimator in a time series regression is unbiased under the first three Gauss-Markov assumptions.(iii) A trending variable ca

7、nnot be used as the dependent variable in multiple regression analysis.5. The following three equations were estimated using the 1,534 observations in 401K.RAW:(0.78) (0.52) (0.045) (0.00004)=0.100, =0.098.(1.95) (0.51) (0.044) (0.28)= 0.144, =0.142. (0.78) (0.52) (0.045) (0.00009) (0.0000000010)=0.

8、108, =0.106.Which of these three models do you prefer. Why?6. Consider the following regression output： where Y = labor force participation rate (LFPR) of women in 1972 and X = LFPR of women in 1968. The regression results were obtained from a sample of 19 cities in the United States.(i) How do you

9、interpret this regression?(ii) Test the hypothesis: against . Which test do you use? And why? What are the underlying assumptions of the test(s) you use?(iii) Suppose that the LFPR in 1968 was 0.58 (or 58 percent). On the basis of the regression results given above, what is the mean LFPR in 1972? Es

10、tablish a 95% confidence interval for the mean prediction.(iv) How would you test the hypothesis that the error term in the population regression is normally distribute? Show the necessary calculations.7. The following regression results were based on monthly data over the period January 1978 to Dec

11、ember 1987:p value=(0.7984) (0.0186) p value=(0.0131) where Y = monthly rate of return on Texaco common stock, %, and X = monthly market rate of return,%.(i) What is the difference between the two regression models?(ii) Given the preceding results, would you retain the intercept term in the first mo

12、del? Why or why not?(iii) How would you interpret the slope coefficients in the two models?(iv) What is the theory underlying the two models?(v) Can you compare the terms of the two models? Why or why not?8. Consider the following models.Model A: Model B: (i) Will OLS estimates of and be the same? W

13、hy?(ii) Will OLS estimates of and be the same? Why?(iii) What is the relationship between and ?(iv) Can you compare the terms of the two models? Why or why not?9. From a sample of 209 firms, Wooldridge obtained the following regression results.se = (0.32) (0.035) (0.0041) (0.00054) = 0.283where sala

14、ry = salary of CEOsales = annual firm salesroe = return on equity in percentros = return on firms stockand where figures in the parentheses are the estimated standard errors.(i) Interpret the preceding regression taking into account any prior expectations that you may have about the signs of the var

15、ious coefficients.(ii) Which of the coefficients are individually statistically significant at the 5 percent level?(iii) What is the overall significance of the regression? Which test do you use? And why?(iv) Can you interpret the coefficients of roe and ros as elasticity coefficients? Why or why not?10. Consider the following modelwhere Y = annual salary of a college professorX = years of teaching experienceD = dummy for genderConsider three ways of defining the dummy variable.