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We Helped With This R Studio Economics Assignment: Have A Similar One?
|Subject||R | R Studio|
|More Info||Help With Macroeconomics In R|
ECON 318 Homework 3
Due Mar. 7th in Class
Instructor: Yu-Wei Hsieh
Note: (1) Please submit your homework in either word or pdf format in class. (2) Please include all your answers, analysis, code and the key steps, for instance the tables/plots produced by R (3) Plagiarism is not accepted. Any similar homework will get zero point.
(4) You can only use R.
Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe, in percentage form), and return on the firm’s stock (ros, in percentage form):
log(salary) = β0 + β1 log(sales) + β2roe + β3ros + u
Use data in “CEO” to estimate this model and answer the following questions.
1. Test the null hypothesis that ros has no effect on salary using the estimated model. Would you include ros in a final model explaining CEO compensation in terms of firm performance?
The data “Houseprice” are for houses that sold during 1981 in North Andover, Massachusetts. 1981 was the year construction began on a local garbage incinerator.
1. To study the effects of the incinerator location on housing price, consider the simpleregression model log(price) = β0 + β1 log(dist) + u
Where price is housing price in dollars and dist is the distance from the house to the incinerator measured in feet. Interpret this equation causally, what sign do you expect for β1 if the presence of the incinerator depresses housing prices? Estimate the equation and
interpret the results.
2. Now add the variables log(intst), log(area), log(land), rooms, baths and age where intst is distance from the home to the interstate, area is square footageof the house, land is the lot size in square feet, rooms is total number of rooms, baths is number of bathrooms and
age is age of the house in years. What do you conclude about the effects of the incinerator?
Use the data in SLEEP75 to study whether there is a tradeoff between the time spent sleeping and the time spent in paid work. Variable definitions are included in SLEEP75 variable definition.pdf
1. Estimate the model sleep = β0 + β1totwrk + u, Where sleep is minutes spent sleeping at night per week and totwrk is total minutes worked during the week. Interpret the estimated β0 and β1.
2. Now estimate the model sleep = β0 + β1totwrk + β2educ + β3age + u, where sleep and totwrk are measured in minutes per week and educ and age are measured in years
3. If someone works five more hours per week, by how many minutes is sleep predicted tochange? Is it a large tradeoff?
4. Discuss the sign and magnitude of the estimated coefficient on educ.
5. Discuss the sign and magnitude of the estimated coefficient on age.
Professor Hsieh decides to run an experiment to measure the effect of time pressure on final exam scores. He gives each of the 50 students in his course the same final exam, but some students have 90 minutes to complete the exam, while the others have 120 minutes. Each student is randomly assigned one of the examination times based on the flip of a coin (25 students will be assigned to the 90 minutes group and vice versa). Let Yi denote the test score of student i and let Xi denote the amount of time assigned to student i (Xi = 90 or 120). Consider the regression model Yi = α + βXi + ui.
1. Explain why E[ui|Xi] = 0 for this regression model.
2. Instead of flipping a coin, Prof. Hsieh decides to assign 90 minutes to junior and 120 minutes to senior. Will this cause any problem?
3. It is reasonable to assume that senior students have higher math ability in general as theymight have completed more math-related courses. If so, will the assignment in (B) lead to upward or downward bias of OLS estimation? Hinet: think about the correlation of ui and Xi. Is it positive or negative? Read the class handout about population regression.
age black case clerical construc educ earns74 gdhlth inlf leis1 leis2 leis3 smsa lhrwage lothinc male marr prot rlxall selfe sleep slpnaps south spsepay spwrk75 totwrk union worknrm workscnd exper yngkid yrsmarr hrwage agesq
1. age in years
2. black =1 if black
3. case identifier
4. clerical =1 if clerical worker
5. construc =1 if construction worker 6. educ years of schooling
7. earns74 total earnings, 1974
8. gdhlth =1 if in good or excellent health
9. inlf =1 if in labor force
10. leis1 sleep ‐ totwrk
11. leis2 slpnaps ‐ totwrk
12. leis3 rlxall ‐ totwrk
13. smsa = 1 if live in smsa
14. lhrwage log hourly wage 15. lothinc log othinc, unless othinc < 0
16. male = 1 if male
17. marr = 1 if married
18. prot = 1 if Protestant
19. rlxall slpnaps + personal activs
20. selfe =1 if self employed
21. sleep mins sleep at night, per week
22. slpnaps mins sleep, including naps, per week
23. south =1 if live in south
24. spsepay spousal wage income
25. spwrk75 =1 if spouse works
26. totwrk mins worked per week
27. union =1 if belong to union
28. worknrm mins work main job
29. workscnd mins work second job
30. exper age ‐ educ ‐ 6
31. yngkid =1 if children < 3 present
32. yrsmarr years married
33. hrwage hourly wage 34. agesq age^2