"econ41502-16

"econ41502-16 - Quantitative Demand Analysis In...

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Quantitative Demand Analysis In Class Exercise Economics 415 – Spring 2010 You believe the demand for your product (X) is linear, dependent upon the price of your good (P X ), the price of good Y (P Y ), the price of good Z (P Z ), average household income (I) in your market, and the level of advertising expenditures (A). I.e., the true relationship is Q X = α 0 + α 1 P X + α 2 P Y + α 3 P Z + α 4 I + α 5 A + ε Here ε is an error with expected value of zero. Statisticians use data available to the firm and give you the estimated equation (standard errors of each parameter estimate are included in parentheses below the estimate): Q X = 1500.0 – 2.50P X + 0.60P Y – 0.80P Z + 0.024I + 0.042A (625.2) (1.05) (0.21) (0.08) (0.011) (0.018) R 2 = 0.86 F = 31.6 Your experts assure you that the overall regression provides a good fit. The price you are currently charging P X = $800. The firm that produces good Y is charging P Y = 600. The price good Z is P Z = 200. Average income of consumers in your market I = 40,000. Your advertising budget is currently $10,000. 1. Is the estimate of the value of the slope coefficient α 4 statistically significant at a 95 percent confidence level? I.e., can we reject the possibility that α 4 = 0 (in which case the average level of consumer income does not affect Q X , the quantity demanded for your product) with only a 5 percent probability of being wrong in doing so? [Use Michael Baye’s “rule of thumb” in evaluating your t-statistic.] 2.
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"econ41502-16 - Quantitative Demand Analysis In...

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