MACROECONOMICS
10th Edition
ISBN: 9781319106072
Author: Mankiw
Publisher: MAC HIGHER
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Question
Chapter 13, Problem 8PA
(a)
To determine
The shape of the LM* curve.
(b)
To determine
The effect of the expansionary fiscal policy under floating exchange rate.
(c)
To determine
The effect of the increased risk premium and interest rate on exchange rate, price level, aggregate income in this model and in normal.
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- 4. Examine the regression table below before answering the questions that follow.Throughout, the Log() function represents the natural logarithm, so that Log(e) =1:Dependent Variable: Log(Expenditures on Cigarettes + 1)Method: Least SquaresVariable Coefficient Std. ErrorConstant 0.50 0.41Log(Income+1) −0.02 0.002(a) Why are the dependent and explanatory variables in the form log(1+x), ratherthan log(x)? (b) Which of the above coefficients are statistically significant? How do you know?(c) Interpret the coefficient on Log(Income+1). (d) What is the predicted level of Log(Expenditures on Cigarettes + 1) for anobserved individual with income of e10 − 1? (4arrow_forward3. Consider the following three (fictional) data points:Country Ultima Thule Narnia NeverlandGDP per capita (US $, 2021, in thousands) 10 30 20Deaths from COVID-19 as of Jan. 2023, millions 0.24 0.16 0.05(a) What is the slope of the line of best fit through these three points, whereDeaths from COVID-19 is the dependent variable and GDP per capita is theexplanatory variable? (b) The standard error for the slope parameter is 0.009. What does this standarderror measure? (c) Is the slope parameter statistically significant at the 5% level of significance?(The relevant critical value is not the usual value, but 4.303, due to the tinysample size). Explain what this means. (d) Why might this slope parameter be a misleading indicator of the relationshipbetween these two variables?arrow_forwardConsider the following estimated regression equation, where both Rent and Earnings aremeasured in pounds (£) at the individual level (standard errors in parentheses):log(\Rent) = 6.9(0.69)+ 0.9(0.3)log(Earnings),(a) Interpret the coefficient on log(Earnings). (b) If we divided Earnings by 1000, so that it is measured in 1000s of pounds instead of pounds,how would (i) the slope, (ii) the intercept change in the above equation? Now suppose the variable London is added, which is equal to one if an individual iives inLondon, and zero otherwise. The estimated regression equation changes to:log(\Rent) = 6.22(0.622)+ 0.5(0.05)log(Earnings) + 2(0.5)London,(c) Interpret the coefficient on London. (d) Explain why the coefficient on log(Earnings) when London is included in the regression andthe coefficient on log(Earnings) when London is not included in the regression are not thesame.arrow_forward
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