Tutorial 8.pdf - Vula

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SOLUTION:(a) The estimated equation isĝift = −4.55 + 2.17 mailsyear + .0059 giftlast + 15.36 proprespn = 4, 268, R 2 = .0834The R-squared is now about .083, compared with about .014 for the simpleregression case. Therefore, the variables giftlast and propresp help to explainsignificantly more variation in gifts in the sample (although still just over eightpercent).(b) Holding giftlast and propresp fixed, one more mailing per year is estimatedto increase gifts by 2.17 guilders. The simple regression estimate is 2.65, sothe multiple regression estimate is somewhat smaller. Remember, the simpleregression estimate holds no other factors fixed.(c) Because propresp is a proportion, it makes little sense to increase it by one.Such an increase can happen only if propresp goes from zero to one. Instead,consider a .10 increase in propresp, which means a 10 percentage point increase.Then, gift is estimated to be 15.36(.1) ≈ 1.54 guilders higher.(d) The estimated equation isĝift = −7.33 + 1.20 mailsyear − .261 giftlast + 16.20 propresp + .527 avggiftn = 4, 268, R 2 = .2005After controlling for the average past gift level, the effect of mailings becomeseven smaller: 1.20 guilders, or less than half the effect estimated by simpleregression.(e) After controlling for the average of past gifts – which we can view as measuringthe “typical” generosity of the person and is positively related to the currentgift level – we find that the current gift amount is negatively related to the mostrecent gift. A negative relationship makes some sense, as people might follow alarge donation with a smaller one.3. Use the data in WAGE1.DTA to confirm the partialling out interpretation of theOLS estimates by explicitly doing the partialling out for Example 3.2 on page 76 ofWooldridge. This first requires regression educ on exper and tenure and saving theresiduals, ̂r 1 . Then, regress log(wage) on ̂r 1 . Compare the coefficient on ̂r 1 with thecoefficient on educ in the regression of log(wage) on educ, exper, and tenure.6
SOLUTION:The regression of educ on exper and tenure yieldseduc = 13.57 − .074 exper + .048 tenure + ̂r 1n = 526, R 2 = .101.Now, when we regress log(wage) on ̂r 1 we obtainlog (wage) = 1.62 + .092 ̂r 1n = 526, R 2 = .207As expected, the coefficient on in the second regression is identical to the coefficienton educ in equation (3.19) on page 76 of Wooldridge. Notice that the R-squared fromthe above regression is less than that in (3.19). In effect, the regression of log(wage)on ̂r 1 explains log(wage) using only the part of educ that is uncorrelated with experand tenure; separate effects of exper and tenure are not included.7
Page 1: Quantitative Methods for EconomicsT
Page 4 and 5: 2. Use the data in CHARITY.DTA to a
Page 6 and 7: (b) The signs of β 2 and β 3 are
Page 8 and 9: (b) Are the signs on the variables

Tutorial 8.pdf - Vula

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