Bundle: Principles of Macroeconomics, Loose-Leaf Version, 7th + Aplia, 1 term Printed Access Card
7th Edition
ISBN: 9781305134935
Author: N. Gregory Mankiw
Publisher: Cengage Learning
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Chapter 14, Problem 6QR
To determine
Efficient market hypothesis.
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Discuss the different types of resources (natural, human, capital) and how they are allocated in an economy. Identify which resources are scarce and which are abundant, and explain the implications of this scarcity or abundance.
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- Using data from 1988 for houses sold in Andover, Massachusetts, from Kiel and McClain (1995), the following equation relates housing price (price) to the distance from a recently built garbage incinerator (dist): = log(price) 9.40 + 0.312 log(dist) n = 135, R2 = 0.162. Interpretation of the slope coefficient? ► How would our interpretation of the slope coefficient change if distance were measured in metres instead of kilometres?arrow_forwardIf GDP goes up by 1% and the investment component of GDPgoes up by more than 1%, how is the investment share ofGDP changing in absolute terms?▶ In economics, what else is expressed as relative percentagechanges?arrow_forwardCEO Salary and Firm SalesWe can estimate a constant elasticity model relating CEO salary to firm sales. The data set is the same one used in Example 2.3, except we now relate salary to sales. Let sales be annual firm sales, measured in millions of dollars. A constant elasticity model is[2.45]ßßlog (salary) = ß0 + ß0log (sales) + u,where ß1 is the elasticity of salary with respect to sales. This model falls under the simple regression model by defining the dependent variable to be y = log(salary) and the independent variable to be x = log1sales2. Estimating this equation by OLS gives[2.46]log (salary)^=4.822 + 0.257 (sales) n = 209, R2 = 0.211.The coefficient of log(sales) is the estimated elasticity of salary with respect to sales. It implies that a 1% increase in firm sales increases CEO salary by about 0.257%—the usual interpretation of an elasticity.arrow_forward
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