Wooldridge Introductory Econometrics 4Th Manual Solutions Manual
Download link: Test Bank for Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M. Calhoun Editorial Director, Business & Economics: Erin Joyner Editor-in-Chief: Joe Sabatino Executive Editor: Michael Worls Associate Developmental Editor: Julie WarwickIntroductory Econometrics A Modern Approach 6th Edition Wooldridge Solutions Manual Solutions Manual, Instructor Manual, Answer key for all chapters, Appendix chapter, Data Sets - Minitab , Data Sets - R are included. Wooldridge Senior Vice President, LRS/Acquisitions & Solutions Planning: Jack W. Introductory Econometrics: A Modern Approach, Fifth Edition Jeffrey M.
Now, we want to find sat such that colgpa =. (iv) With hsperc fixed, colgpa =. So A is predicted to have a score. (iii) The difference between A and B is simply 140 times the coefficient on sat, because hsperc is the same for both students.
The sample correlation between cigs and faminc is about .173, indicating a negative correlation. On the other, family incomes are also higher for families with more education, and more education and cigarette smoking tend to be negatively correlated. (ii) On the one hand, an increase in income generally increases the consumption of a food, and cigs and faminc could be positively correlated. 3.3(i) If adults trade off sleep for work, more work implies less sleep (other things equal), so1 0 and Corr(x1,x2) 0, as more income typically means better nutrition for the mother and better prenatal care.
93 ounces.) C3.3 (i) The constant elasticity equation isLog( salary ) 4.62 . (The variable faminc is measured in thousands, so $10,000 more in 1988 income increases predicted birth weight by only. This is due to the fact that cigs and faminc are not very correlated, and the coefficient on faminc is practically small. 030.The effect of cigarette smoking is slightly smaller when faminc is added to the regression, but the difference is not great. 093 faminc n 1,388, R 2 . 514 cigs n 1,388, R 2 .
000036 profits n 177, R 2 . When we add it in levels form, we getLog( salary ) 4.69 . 299.(ii) We cannot include profits in logarithmic form because profits are negative for nine of the companies in the sample.
318.This means that one more year as CEO increases predicted salary by about 1.2%. (iii) Adding ceoten to the equation givesLog( salary ) 4.56 . This is certainly not “most” of the variation. Together, these variables (and we could drop profits without losing anything) explain almost 30% of the sample variation in log(salary). However, remember that we are holding sales and market value fixed. Here, profits are measured in millions, so if profits increase by $1 billion, which means profits = 1,000 – a huge change – predicted salary increases by about only 3.6%.
C3.5 The regression of educ on exper and tenure yields educ = 13.57 . Also, profits is a short term measure of how the firm is doing, while mktval is based on past, current, and expected future profitability. Given the fairly substantial correlation between market value and firm profits, it is not too surprising that the latter adds nothing to explaining CEO salaries. As we know, this causes no bias in the OLS estimators, although it can cause their variances to be large.
305 lnchprg n = 408, R2 =. C3.7 (i) The results of the regression areMath10 20.36 6.23 log(expend ) . Notice that the R-squared from the above regression is below that in (3.19).18 In effect, the regression of log(wage) on rˆ1 explains log(wage) using only the part of educ that is uncorrelated with exper and tenure separate effects of exper and tenure are not included. As expected, the coefficient on rˆ1 in the second regression is identical to the coefficient on educ in equation (3.19).
Presumably this is well outside any sensible range. Setting log(expend) = 0 does not make sense, because it is the same as setting expend = 1, and spending is measured in dollars per student. Setting lnchprg = 0 makes sense, as there are schools with low poverty rates. (ii) As usual, the estimated intercept is the predicted value of the dependent variable when all regressors are set to zero.
Because Corr(x1,x2) < 0, which means 1 0 , and ˆ2 0 , the simple regression estimate, , is larger than the multiple regression estimate, ˆ. (v) We can use equation (3.23). This makes sense, especially in 1993 in Michigan, where school funding was essentially determined by local property tax collections. (iv) The sample correlation between lexpend and lnchprg is about .19 , which means that, on average, high schools with poorer students spent less per student. And the estimated spending effect is larger than it was in part (i) – almost double. (iii) The simple regression results areMath10 69.34 11.16 log(expend ) n = 408, R2 =.
The simple regression estimate is 2.65, so the multiple regression estimate is somewhat smaller. Therefore, the variables giftlast and propresp help to explain significantly more variation in gifts in the sample (although still just over eight percent).19 (ii) Holding giftlast and propresp fixed, one more mailing per year is estimated to increase gifts by 2.17 guilders. 014 for the simple regression case. The R-squared is now about. 0059 giftlast 15.36 propresp n 4,268, R 2 . C3.9 (i) The estimated equation isGift 4.55 2.17 mailsyear .
261 giftlast 16.20 propresp . (iv) The estimated equation isGift 7.33 1.20 mailsyear . Then, gift is estimated to be 15.36(.1) 1.54 guilders higher. 10 increase in propresp, which means a 10 percentage point increase. Such an increase can happen only if propresp goes from zero to one. (iii) Because propresp is a proportion, it makes little sense to increase it by one.
The effect of single parenthood seem small. 𝑚𝑎𝑡ℎ4 The percentage of children not in the married-couples families has a negative impact on percentage of satisfactory level of 4th grade math. C3.11 (i) The regression results are: ̂ = 96.7704 − 0.8328𝑝𝑐𝑡𝑠𝑔𝑙𝑒. A negative relationship makes some sense, as people might follow a large donation with a smaller one. (v) After controlling for the average of past gifts – which we can view as measuring the “typical” generosity of the person and is positively related to the current gift level – we find that the current gift amount is negatively related to the most recent gift. 2005 After controlling for the average past gift level, the effect of mailings becomes even smaller: 1.20 guilders, or less than half the effect estimated by simple regression.
(iii) The sample correlation between lmedinc and free is -0.74. This means that, as the percentage of children not in married couples increases, the percentage of satisfactory level of 4th grade math decreases. 𝑚𝑎𝑡ℎ4 The coefficient of pctsgle has negatively increased from -0.8328 to -0.1996. (ii) The estimated regression results are: ̂ = 51.723 − 0.1996𝑝𝑐𝑡𝑠𝑔𝑙𝑒 − 0.3964𝑓𝑟𝑒𝑒 + 3.5601𝑙𝑚𝑒𝑑𝑖𝑛𝑐. 08328 percentage, which is a small effect. 10 (ten percentage points), the percentage of satisfactory level of 4th grade math is estimated to decrease by.
No, this knowledge does not affect the model to study the causal effect of single parenthood on math performance. By comparing the three variables, it is very clear that the variable free has the highest VIF. 1VIFlmedinc = 1−𝑅2 = 1−0.3212 = 1.4732. (iv) No, because high correlations among the variables lmedinc and free do not make it more difficult to determine the causal effect of single parenthood on student performance.(v) VIFpctsgle = 1−𝑅2 = 1−0.3795 = 1.6116.
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