Test Bank for Using Econometrics: A Practical Guide, 7th Edition A H. Studenmund

Test Bank for Using Econometrics: A Practical Guide, 7th Edition A H. Studenmund 2-10. (a) Vi: positive. Hi: negative (although some would argue that in a world of perfect information, drivers would take fewer risks if they knew the state had few hospitals). Ci: ambiguous because a high rate of driving citations could indicate risky driving (raising fatalities) or zealous police citation policies (reducing risky driving and therefore fatalities). (b) No, because the coefficient differences are small and the data will differ from year to year. We’d be more concerned if the coefficients differed by orders of magnitude or changed sign. (c) Since the equation for the second year has similar degrees of freedom and a much lower R , no calculation is needed to know that the equation for the first year has a higher R 2 . Just to be sure, we calculated R 2 2 and obtained 0.652 for the first year and 0.565 for the second year. 2-11. (a) It might seem that the higher the percentage body fat, the higher the weight, holding constant height, but muscle weighs more than fat, so it’s possible that a lean, highly muscled man could weigh more than a less well-conditioned man of the same height. (b) We prefer Equation 1.24 because we don’t think F belongs in the equation on theoretical grounds. The meaning of the coefficient of X changes in that F now is held constant. (c) The fact that R 2 drops when the percentage body fat is introduced to the equation strengthens our preference for Equation 1.24. (d) This is subtle, but since 0.28 times 12.0 equals 3.36, we have reason to believe that the impact of bodyfat on weight (holding constant height) is very small indeed. That is, moving from average bodyfat to no bodyfat would lower your weight by only 3.36 pounds. (b) The meaning of the coefficient of SIZE is that for every increase of one in the size of the student body, we’d expect a college to generate 2.15 more applications, holding RANK and COMMONAP constant. The meaning of the coefficient of RANK is that every one-rank improvement in a college’s U.S. News ranking should generate 32.1 more applications, holding SIZE and COMMONAP constant. These results do not allow us to conclude that a college’s ranking is 15 times more important than the size of that college because the units of the variables SIZE and RANK are quite different in magnitude. On a more philosophical level, it’s risky to draw any general conclusions at all from one regression estimated on a sample of 49 colleges. (c) The meaning of the coefficient of COMMONAP is that a college that switches to using the common application can expect to generate 1222 more applications, holding constant RANK and SIZE. However, this result does not prove that a given college would increase applications by 1222 by switching to the common application. Why not? First, we don’t trust this result because there may well be an omitted relevant variable (or two) and because all but three of the colleges in the sample use the common application. Second, in general, econometric results are evidence that can be used to support an argument, but in and of themselves they don’t come close to “proving” anything. (e) If you drop COMMONAP from the equation, R 2 falls from 0.681. This is evidence (but not proof) that COMMONAP belongs in the equation.