Microeconomics
11th Edition
ISBN: 9781260507140
Author: David C. Colander
Publisher: McGraw Hill Education
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Chapter 17.W1, Problem 6Q
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Determine the current bond worth.
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2. A firm’s production function is given by:Q = 10KLThe unit capital and labour costs are 2 and 1 pounds respectively. The firm is contracted to produce2000 units.(a) Write out the optimisation problem of the firm. (b) Express this problem using a Lagrangian function. (c) Find values of K and L which fulfil the contract with minimal cost to the firm. (d) Calculate the total cost to the firm.
3. Consider the following estimated regression equation, estimated using a sample of firms, where RDis total firm spending on research and development in USD ($), Revenue is total firm revenuein USD ($), and W ages is the firms’ total spending on wages in USD ($) (standard errors inparentheses):RDd = 1000(600)+ 0.5(0.1)Revenue + 1.5(0.5)W ages,(a) Interpret the coefficients on each of the explanatory variables. (b) Which of the three coefficients are statistically significant at the 5% level of significance? Howdo you know? A researcher runs a two-sided statistical test of the null hypothesis that both the coefficients onthe explanatory variables above are jointly equal to 0.25 (mathematically, that β1 = β2 = 0.25),and reports a p-value of 0.045.(c) What does this p-value mean for the outcome of the test? (d) What would an appropriate two-sided alternative hypothesis look like?
4. Consider the following regression equation, where Google is equal to 1 if an individual in thesample has worked at Google and 0 otherwise, and Earnings is annual earnings in thousands ofpounds (standard errors in parentheses):Earnings \ = 25000(12.5)+ 42000(7.0)Google,(a) Interpret the coefficient on Google.(b) Is the coefficient on Google statistically significant at the 5% level? How do you know?(c) Suppose that instead of Google we had used a variable called NeverGoogle, equal to 1 if anindividual has never worked at Google and 0 otherwise. (i) How would the slope coefficientchange? (ii) What would happen to the intercept? (d) What prevents us from interpreting the coefficient on Google as a causal effect? Give examplesin your answer.
Chapter 17 Solutions
Microeconomics
Ch. 17.1 - Prob. 1QCh. 17.1 - Prob. 2QCh. 17.1 - Prob. 3QCh. 17.1 - Prob. 4QCh. 17.1 - Prob. 5QCh. 17.1 - Prob. 6QCh. 17.1 - Prob. 7QCh. 17.1 - Prob. 8QCh. 17.1 - Prob. 9QCh. 17.1 - Prob. 10Q
Ch. 17.A - Prob. 1QECh. 17.A - Prob. 2QECh. 17.A - Prob. 3QECh. 17.A - Prob. 4QECh. 17.A - Prob. 5QECh. 17.A - Prob. 6QECh. 17.A - Prob. 7QECh. 17.A - Prob. 8QECh. 17.W - Prob. 1QECh. 17.W - Prob. 2QECh. 17.W - Prob. 3QECh. 17.W - Prob. 4QECh. 17.W - Prob. 5QECh. 17.W - Prob. 6QECh. 17.W - Prob. 7QECh. 17.W - Prob. 8QECh. 17.W - Prob. 9QECh. 17.W - Prob. 10QECh. 17.W - Prob. 1QAPCh. 17.W - Prob. 2QAPCh. 17.W - Prob. 3QAPCh. 17.W - Prob. 4QAPCh. 17.W - Prob. 5QAPCh. 17.W - Prob. 1IPCh. 17.W - Prob. 2IPCh. 17.W - Prob. 3IPCh. 17.W - Prob. 4IPCh. 17.W1 - Prob. 1QCh. 17.W1 - Prob. 2QCh. 17.W1 - Prob. 3QCh. 17.W1 - Prob. 4QCh. 17.W1 - Prob. 5QCh. 17.W1 - Prob. 6QCh. 17.W1 - Prob. 7QCh. 17.W1 - Prob. 8QCh. 17.W1 - Prob. 9QCh. 17.W1 - Prob. 10QCh. 17 - Prob. 1QECh. 17 - Prob. 2QECh. 17 - Prob. 3QECh. 17 - Prob. 4QECh. 17 - Prob. 5QECh. 17 - Prob. 6QECh. 17 - Prob. 7QECh. 17 - Prob. 8QECh. 17 - Prob. 9QECh. 17 - Prob. 10QECh. 17 - Prob. 11QECh. 17 - Prob. 12QECh. 17 - Prob. 13QECh. 17 - Prob. 14QECh. 17 - Prob. 15QECh. 17 - Prob. 16QECh. 17 - Prob. 17QECh. 17 - Prob. 18QECh. 17 - Prob. 19QECh. 17 - Prob. 20QECh. 17 - Prob. 21QECh. 17 - Prob. 22QECh. 17 - Prob. 23QECh. 17 - Prob. 24QECh. 17 - Prob. 25QECh. 17 - Prob. 26QECh. 17 - Prob. 1QAPCh. 17 - Prob. 2QAPCh. 17 - Prob. 3QAPCh. 17 - Prob. 4QAPCh. 17 - Prob. 5QAPCh. 17 - Prob. 6QAPCh. 17 - Prob. 1IPCh. 17 - Prob. 2IPCh. 17 - Prob. 3IPCh. 17 - Prob. 4IPCh. 17 - Prob. 5IPCh. 17 - Prob. 6IPCh. 17 - Prob. 7IPCh. 17 - Prob. 8IPCh. 17 - Prob. 9IPCh. 17 - Prob. 10IPCh. 17 - Prob. 11IP
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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
- 3. Consider the following regression equation, where Cigs is daily spending on cigarettes in poundssterling (£), Y earsEduc is years of education, and F emale equals one if an individual is femaleand zero otherwise (standard errors in parentheses):Cigs [ = 4(1.6)− 0.08(0.032)Y earsEduc − 0.5(0.2)F emale,(a) Interpret the coefficients on Y earsEduc and F emale. (b) What does the model predict the average daily spending on cigarettes would be for womenwith 12 years of education? (c) Form the 95% confidence interval for the coefficient on F emale. (d) Economists are often interested in estimating production functions of the Cobb-Douglas form(Yiis the ith firm’s output, Liits spending on labour, and Kiits spending on capital):Yi = ALαi Kβi,How might someone estimate α and β from this equation using linear regression?arrow_forward2. The demand and supply functions for two interdependent goods X and Y are givenbyQDX = 7 − 4PX + 2PYQSX = −6 + 4PX − PYQDY = 1 + PX − PYQSY = −4 − PX + 2PY(a) Find the market equilibrium condition for each good. (b) Express the equilibrium conditions in the matrix form Ax = b.(c) Find the inverse of matrix A. (d) Given your result in part (c), calculate the equilibrium prices. (e) What is the equilibrium quantity for goods X and Y ?arrow_forward2. A two-sector macro-economic model satisfies the following structural equations:Y = C + I∗C = aY + bwhere 0 < a < 1 and b > 0 are parameters and I∗ denotes investment.(a) What are the exogenous and endogenous variables in this model? (b) Re-arrange this system of equations to express the endogenous variables in terms of the exogenous variables and parameters. (c) Express this system of equations in the matrix form Ax = b. (d) Show that the inverse of matrix A exists.arrow_forward
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