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    Demand for Jets and Elasticity

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    Using data from the 1980's two economists, using regression analysis, estimated the demand for new corporate jets. The demand function hypothesized that the demand for corporate jets (Qd) depended upon the price of new jets (P), the price of used jets (Pr), corporate profits (M) and a dummy variable representing corporate tax laws (D). The equation estimated was Qd = a + bP +cPr +dM +eD.

    1. What do the results tell you about regression estimation and the demand for new corporate jets?
    Qd =17.33 - .00016P + .0005Pr - .85M + 31.99D
    (0.40) (-3.9) (4.81) (-1.17) (3.66)
    R2 =.86 and F = 20.35

    2. Using the data and regression results the authors estimated both their own price elasticity and cross-price elasticity of demand; ep = -3.95 and epr = 6.41. What information does this provide about the demand for jets?

    3. If you were to replicate this study today would you expect similar results? Why or why not? What variable would you add to improve the significance of the estimation? Why?

    4.Briefly explain the relevance of each independent variable to Qd and the expected signs for each variable's coefficient.

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    Solution Preview

    1. The results tell us that there is a strong negative and significant correlation between the price of a jet and its demand (which we would expect, due to downward sloping demand curves), a positive and significant correlation between the price of used jets and demand (also which makes sense, because it shows there are less substitutes and also used jets hold their value well), a negative and insignificant relationship between corporate profits and demand for a jet (meaning that either corporate profits slightly decrease the demand for jets, or demands for jets decrease profits... either way, it's ...

    Solution Summary

    This solution looks at a regression equation that estimates the demand for jets as a function of other variables. Additionally, this response helps to solve for the meaning of elasticity and considers how the regression could be improved.