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Unformatted text preview: Department of Economics Spring 2006 University of California Prof. Woroch Economics 140 MIDTERM #2 EXAM CODE #4 GENERAL INSTRUCTIONS : Write your name, your GSIs name and the above Exam Code on the front cover of two blue books. Mark one as Blue Book #1 and the other as Blue Book #2. Put Part I in blue book #1 and Part II in #2. There is a total of 100 points with point assignments given in the instructions for each part. You may want to make preliminary calculations on scratch paper, but be sure to put all of your answers in the bluebook. I. AGREE, DISAGREE and EXPLAIN : Choose 3 of the following 4 statements regarding a research project involving regression analysis . For each, decide whether you agree or disagree with part or all of the statement and explain the reasoning behind your answer in a couple short paragraphs . Note that you may agree with part of the statement, but disagree with the rest. Each question is worth 14 points for a total of 42. 1. When estimating the demand for gasoline using data on consumption by state and by month, it occurs to the researcher that weather conditions may be a significant explanatory variable. The researcher should decide to include a measure of weather conditions if its coefficient estimate is statistically significantly different from zero, or the R 2 increases as a result, or both. An increase in R 2 is uninformative about whether the variable is a significant explanatory variable because the R 2 always increases when an additional variable is added to the model. Looking at the adjusted R 2 is a valid way of comparing the restricted and unrestricted models because, unlike the R 2 , the adjusted R 2 will decrease if variables that do not improve the fit are added. However, the test of statistical significance is typically preferred as the significance level can be specified. Because of this, I might exclude the weather variable if its coefficient is insignificant but the adjusted R 2 is greater in the unrestricted model. 2. Believing that advertising stimulates sales, a researcher regressed sales of breakfast cereal brands against the amount spent on advertising by each brand. If, in fact, the relationship between sales and advertising was quadratic and not simply linear, then the coefficient on the researchers linear regression will be biased, and is likely to be too small. True. If we dont include the quadratic term then we have specified the functional form incorrectly. In this case, the misspecification creates omitted variable bias in the parameter on the linear term. Let X 1 be the linear term and X 2 the quadratic term. The OLS estimator of 1 in the linear model can be expressed in terms of the population parameter and a bias term: ) ( ) , ( 1 2 1 2 1 1 X Var X X Cov p + The covariance between the linear and quadratic terms is positive as long as advertising spending only takes on positive values. 2 gives the linear relationship between the quadratic term and...
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This note was uploaded on 10/21/2008 for the course ECON 140 taught by Professor Duncan during the Spring '08 term at University of California, Berkeley.
- Spring '08