計量經濟學導論伍德里奇第三版課后習題答案完整版

1、CHAPTER 1SOLUTIONS TO PROBLEMS1.1(i) Ideally, we could randomly assign students to classes of different sizes. That is, each student is assigned a different class size without regard to any student characteristics such as ability and family background. For reasons we will see in Chapter 2, we would

2、like substantial variation in class sizes (subject, of course, to ethical considerations and resource constraints).(ii) A negative correlation means that larger class size is associated with lower performance. We might find a negative correlation because larger class size actually hurts performance.

3、 However, with observational data, there are other reasons we might find a negative relationship. For example, children from more affluent families might be more likely to attend schools with smaller class sizes, and affluent children generally score better on standardized tests. Another possibility

4、 is that, within a school, a principal might assign the better students to smaller classes. Or, some parents might insist their children are in the smaller classes, and these same parents tend to be more involved in their childrens education.(iii) Given the potential for confounding factors some of

5、which are listed in (ii) finding a negative correlation would not be strong evidence that smaller class sizes actually lead to better performance. Some way of controlling for the confounding factors is needed, and this is the subject of multiple regression analysis.1.2(i) Here is one way to pose the

6、 question: If two firms, say A and B, are identical in all respects except that firm A supplies job training one hour per worker more than firm B, by how much would firm As output differ from firm Bs?(ii) Firms are likely to choose job training depending on the characteristics of workers. Some obser

7、ved characteristics are years of schooling, years in the workforce, and experience in a particular job. Firms might even discriminate based on age, gender, or race. Perhaps firms choose to offer training to more or less able workers, where “ability” might be difficult to quantify but where a manager

8、 has some idea about the relative abilities of different employees. Moreover, different kinds of workers might be attracted to firms that offer more job training on average, and this might not be evident to employers.(iii) The amount of capital and technology available to workers would also affect o

9、utput. So, two firms with exactly the same kinds of employees would generally have different outputs if they use different amounts of capital or technology. The quality of managers would also have an effect.(iv) No, unless the amount of training is randomly assigned. The many factors listed in parts

10、 (ii) and (iii) can contribute to finding a positive correlation between output and training even if job training does not improve worker productivity.1.3It does not make sense to pose the question in terms of causality. Economists would assume that students choose a mix of studying and working (and

11、 other activities, such as attending class, leisure, and sleeping) based on rational behavior, such as maximizing utility subject to the constraint that there are only 168 hours in a week. We can then use statistical methods to measure the association between studying and working, including regressi

12、on analysis that we cover starting in Chapter 2. But we would not be claiming that one variable “causes” the other. They are both choice variables of the student.CHAPTER 2SOLUTIONS TO PROBLEMS2.1(i) Income, age, and family background (such as number of siblings) are just a few possibilities. It seem

13、s that each of these could be correlated with years of education. (Income and education are probably positively correlated; age and education may be negatively correlated because women in more recent cohorts have, on average, more education; and number of siblings and education are probably negative

14、ly correlated.)(ii) Not if the factors we listed in part (i) are correlated with educ. Because we would like to hold these factors fixed, they are part of the error term. But if u is correlated with educ then E(u|educ) 0, and so SLR.4 fails.2.2In the equation y = b0 + b1x + u, add and subtract a0 fr

15、om the right hand side to get y= (a0 + b0) + b1x + (u-a0). Call the new error e= u-a0, so that E(e)= 0. The new intercept is a0+ b0, but the slope is still b1.2.3(i) Let yi= GPAi, xi= ACTi, and n= 8. Then = 25.875, = 3.2125, (xi )(yi )= 5.8125, and (xi )2= 56.875. From equation (2.9), we obtain the

16、slope as = 5.8125/56.875 .1022, rounded to four places after the decimal. From (2.17), = 3.2125 (.1022)25.875 .5681. So we can write = .5681 + .1022 ACTn = 8.The intercept does not have a useful interpretation because ACT is not close to zero for the population of interest. If ACT is 5 points higher

17、, increases by .1022(5)= .511.(ii) The fitted values and residuals rounded to four decimal places are given along with the observation number i and GPA in the following table:iGPA12.82.7143.085723.43.0209.379133.03.2253.225343.53.3275.172553.63.5319.068163.03.1231.123172.73.1231.423183.73.6341.0659Y

18、ou can verify that the residuals, as reported in the table, sum to -.0002, which is pretty close to zero given the inherent rounding error.(iii) When ACT = 20, = .5681 + .1022(20) 2.61. (iv) The sum of squared residuals, , is about .4347 (rounded to four decimal places), and the total sum of squares

19、, (yi )2, is about 1.0288. So the R-squared from the regression isR2 = 1 SSR/SST 1 (.4347/1.0288) .577.Therefore, about 57.7% of the variation in GPA is explained by ACT in this small sample of students.2.4 (i) When cigs = 0, predicted birth weight is 119.77 ounces. When cigs= 20, = 109.49. This is

20、about an 8.6% drop.(ii) Not necessarily. There are many other factors that can affect birth weight, particularly overall health of the mother and quality of prenatal care. These could be correlated with cigarette smoking during birth. Also, something such as caffeine consumption can affect birth wei

21、ght, and might also be correlated with cigarette smoking.(iii) If we want a predicted bwght of 125, then cigs = (125 119.77)/( .524) 10.18, or about 10 cigarettes! This is nonsense, of course, and it shows what happens when we are trying to predict something as complicated as birth weight with only

22、a single explanatory variable. The largest predicted birth weight is necessarily 119.77. Yet almost 700 of the births in the sample had a birth weight higher than 119.77.(iv) 1,176 out of 1,388 women did not smoke while pregnant, or about 84.7%. Because we are using only cigs to explain birth weight

23、, we have only one predicted birth weight at cigs = 0. The predicted birth weight is necessarily roughly in the middle of the observed birth weights at cigs = 0, and so we will under predict high birth rates.2.5 (i) The intercept implies that when inc= 0, cons is predicted to be negative $124.84. Th

24、is, of course, cannot be true, and reflects that fact that this consumption function might be a poor predictor of consumption at very low-income levels. On the other hand, on an annual basis, $124.84 is not so far from zero.(ii) Just plug 30,000 into the equation: = 124.84 + .853(30,000)= 25,465.16

25、dollars.(iii) The MPC and the APC are shown in the following graph. Even though the intercept is negative, the smallest APC in the sample is positive. The graph starts at an annual income level of $1,000 (in 1970 dollars).2.6 (i) Yes. If living closer to an incinerator depresses housing prices, then

26、 being farther away increases housing prices.(ii) If the city chose to locate the incinerator in an area away from more expensive neighborhoods, then log(dist) is positively correlated with housing quality. This would violate SLR.4, and OLS estimation is biased.(iii) Size of the house, number of bat

27、hrooms, size of the lot, age of the home, and quality of the neighborhood (including school quality), are just a handful of factors. As mentioned in part (ii), these could certainly be correlated with dist and log(dist).2.7 (i) When we condition on inc in computing an expectation, becomes a constant

28、. So E(u|inc)= E(e|inc) = E(e|inc)= 0 because E(e|inc)= E(e)= 0.(ii) Again, when we condition on inc in computing a variance, becomes a constant. So Var(u|inc)= Var(e|inc)= ()2Var(e|inc)= inc because Var(e|inc)= .(iii) Families with low incomes do not have much discretion about spending; typically,

29、a low-income family must spend on food, clothing, housing, and other necessities. Higher income people have more discretion, and some might choose more consumption while others more saving. This discretion suggests wider variability in saving among higher income families.2.8 (i) From equation (2.66)

30、, = /.Plugging in yi = b0 + b1xi + ui gives = /.After standard algebra, the numerator can be written as.Putting this over the denominator shows we can write as = b0/ + b1 + /.Conditional on the xi, we haveE() = b0/ + b1because E(ui) = 0 for all i. Therefore, the bias in is given by the first term in

31、 this equation. This bias is obviously zero when b0= 0. It is also zero when = 0, which is the same as = 0. In the latter case, regression through the origin is identical to regression with an intercept.(ii) From the last expression for in part (i) we have, conditional on the xi,Var()= Var = = = / .

32、(iii) From (2.57), Var() = s2/. From the hint, , and so Var() Var(). A more direct way to see this is to write = , which is less than unless = 0.(iv) For a given sample size, the bias in increases as increases (holding the sum of the fixed). But as increases, the variance of increases relative to Va

33、r(). The bias in is also small when is small. Therefore, whether we prefer or on a mean squared error basis depends on the sizes of , , and n (in addition to the size of ).2.9(i) We follow the hint, noting that = (the sample average of is c1 times the sample average of yi) and = . When we regress c1

34、yi on c2xi (including an intercept) we use equation (2.19) to obtain the slope:From (2.17), we obtain the intercept as = (c1) (c2)= (c1) (c1/c2)(c2)= c1( )= c1) because the intercept from regressing yi on xi is ( ).(ii) We use the same approach from part (i) along with the fact that = c1+ and = c2+

35、. Therefore, = (c1 + yi) (c1+ )= yi and (c2+ xi) = xi . So c1 and c2 entirely drop out of the slope formula for the regression of (c1+ yi) on (c2+ xi), and = . The intercept is = = (c1+ ) (c2+ )= ()+ c1 c2= + c1 c2, which is what we wanted to show.(iii) We can simply apply part (ii) because . In oth

36、er words, replace c1 with log(c1), yi with log(yi), and set c2 = 0.(iv) Again, we can apply part (ii) with c1 = 0 and replacing c2 with log(c2) and xi with log(xi). If are the original intercept and slope, then and .2.10 (i) This derivation is essentially done in equation (2.52), once is brought ins

37、ide the summation (which is valid because does not depend on i). Then, just define .(ii) Because we show that the latter is zero. But, from part (i), Because the are pairwise uncorrelated (they are independent), (because ). Therefore, (iii) The formula for the OLS intercept is and, plugging in gives

38、 (iv) Because are uncorrelated,which is what we wanted to show.(v) Using the hint and substitution gives 2.11 (i) We would want to randomly assign the number of hours in the preparation course so that hours is independent of other factors that affect performance on the SAT. Then, we would collect in

39、formation on SAT score for each student in the experiment, yielding a data set , where n is the number of students we can afford to have in the study. From equation (2.7), we should try to get as much variation in as is feasible.(ii) Here are three factors: innate ability, family income, and general

40、 health on the day of the exam. If we think students with higher native intelligence think they do not need to prepare for the SAT, then ability and hours will be negatively correlated. Family income would probably be positively correlated with hours, because higher income families can more easily a

41、fford preparation courses. Ruling out chronic health problems, health on the day of the exam should be roughly uncorrelated with hours spent in a preparation course.(iii) If preparation courses are effective, should be positive: other factors equal, an increase in hours should increase sat.(iv) The

42、intercept, , has a useful interpretation in this example: because E(u) = 0, is the average SAT score for students in the population with hours = 0.CHAPTER 3SOLUTIONS TO PROBLEMS3.1(i) hsperc is defined so that the smaller it is, the lower the students standing in high school. Everything else equal,

43、the worse the students standing in high school, the lower is his/her expected college GPA.(ii) Just plug these values into the equation: = 1.392 - .0135(20) + .00148(1050) = 2.676.(iii) The difference between A and B is simply 140 times the coefficient on sat, because hsperc is the same for both stu

44、dents. So A is predicted to have a score .00148(140) .207 higher.(iv) With hsperc fixed, = .00148Dsat. Now, we want to find Dsat such that = .5, so .5= .00148(Dsat) or Dsat= .5/(.00148) 338. Perhaps not surprisingly, a large ceteris paribus difference in SAT score almost two and one-half standard de

45、viations is needed to obtain a predicted difference in college GPA or a half a point.3.2(i) Yes. Because of budget constraints, it makes sense that, the more siblings there are in a family, the less education any one child in the family has. To find the increase in the number of siblings that reduce

46、s predicted education by one year, we solve 1 = .094(Dsibs), so Dsibs= 1/.094 10.6.(ii) Holding sibs and feduc fixed, one more year of mothers education implies .131 years more of predicted education. So if a mother has four more years of education, her son is predicted to have about a half a year (

47、.524) more years of education.(iii) Since the number of siblings is the same, but meduc and feduc are both different, the coefficients on meduc and feduc both need to be accounted for. The predicted difference in education between B and A is .131(4)+ .210(4)= 1.364.3.3(i) If adults trade off sleep f

48、or work, more work implies less sleep (other things equal), so 0.(ii) The signs of and are not obvious, at least to me. One could argue that more educated people like to get more out of life, and so, other things equal, they sleep less ( 0, 0. Both LSAT and GPA are measures of the quality of the ent

49、ering class. No matter where better students attend law school, we expect them to earn more, on average. , 0. The number of volumes in the law library and the tuition cost are both measures of the school quality. (Cost is less obvious than library volumes, but should reflect quality of the faculty,

50、physical plant, and so on.)(iii) This is just the coefficient on GPA, multiplied by 100: 24.8%.(iv) This is an elasticity: a one percent increase in library volumes implies a .095% increase in predicted median starting salary, other things equal.(v) It is definitely better to attend a law school wit

51、h a lower rank. If law school A has a ranking 20 less than law school B, the predicted difference in starting salary is 100(.0033)(20)= 6.6% higher for law school A.3.5(i) No. By definition, study+ sleep+ work+ leisure= 168. Therefore, if we change study, we must change at least one of the other categories so that the sum is still 168.(ii) From part (i), we can write, say, study as a