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# We Helped With This R Studio Economics Assignment: Have A Similar One?

Category | Economics |
---|---|

Subject | R | R Studio |

Difficulty | Undergraduate |

Status | Solved |

More Info | Help With Macroeconomics In R |

## Assignment Description

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.

### Q1

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*) = *β*_{0 }+ *β*_{1 }log(*sales*) + *β*_{2}*roe *+ *β*_{3}*ros *+ *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?*

### Q2

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*) = *β*_{0 }+ *β*_{1 }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 β*_{1 }*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?*

1

### Q3

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 *= *β*_{0 }+ *β*_{1}*totwrk *+ *u, Where sleep is minutes spent sleeping at night per week and totwrk
is total minutes worked during the week. Interpret the estimated β*_{0 }*and
β*_{1}*.*

*2. **Now estimate the model sleep *= *β*_{0 }+ *β*_{1}*totwrk *+ *β*_{2}*educ *+ *β*_{3}*age *+ *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.*

### Q4

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 *Y _{i }*denote
the test score of student

*i*and let

*X*denote the amount of time assigned to student

_{i }*i*(

*X*= 90 or 120). Consider the regression model

_{i }*Y*=

_{i }*α*+

*βX*+

_{i }*u*.

_{i}*1. **Explain why E*[*u _{i}|X_{i}*]
= 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 u _{i }and X_{i}. Is it positive
or negative? Read the class handout about population regression.*

2

## Assignment Description

2/15/2017 fmwww.bc.edu/ecp/data/wooldridge/sleep75.des

SLEEP75.DES

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

Obs: 706

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

http://fmwww.bc.edu/ecp/data/wooldridge/sleep75.des 1/1