# 代写EC 421课程作业、代做R编程语言作业、代写OLS Review作业、代做R留学生作业代写Web开发|调试Web开发

Problem Set 1: OLS Review
EC 421: Introduction to Econometrics
Due before midnight on Sunday, 19 April 2020
IMPORTANT You must submit two files:
1. your typed responses/answers to the question (in a Word file or something similar)
2. the R script you used to generate your answers. Each student must turn in her/his own answers.
README! The data in this problem set come from the 2018 American Community Survey (ACS), which I downloaded
from IPUMS. The last page has a table that describes each variable in the dataset(s).
OBJECTIVE This problem set has three purposes: (1) reinforce the metrics topics we reviewed in class; (2) build
INTEGRITY If you are suspected of cheating, then you will receive a zero. We may report you to the dean.
Setup
Q01. Load your packages. You'll probably going to need/want tidyverse and here (among others).
Q02. Now load the data. I saved the same dataset as two different formats:
readr package in the tidyverse .
readr package in the tidyverse .
Q03. Check your dataset. How many observations and variables do you have? Hint: Try dim() , ncol() , nrow() .
Q04. Plot a histogram of households' income (variable: hh_income ). Note: Household income is in tens of
thousands of dollars (so a value of 8 implies an income of \$80,000.)
Q05. What are the mean and median levels of household income? Based upon this answer and the previous
histogram, is household income (fairly) evenly distributed or is it more skewed? Explain your answer.
Q06. Create a histogram of household income per capita—meaning the household's income divided by the number
of individuals in the household. Does dividing by the number of individuals in the household change your
Q07. Run a regression that helps summarize the relationship between household income and household size.
Interpret the results of the regression—the meaning of the coefficient(s). Comment on the coefficient's statistical
significance.
Q08. Explain why you chose the specification you chose in the previous question.
Was it linear, log-linear, log-log?
What was the outcome variable?
What was the explanatory variable?
Why did you make these choices?
Q09. Plot a histogram of the time households spend commuting each day (the variable time_commuting is the
average commuting time for a household). Is the distribution of commute time more or less equitable than
income? Explain.
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Regression refresher: Varying the specification
Q10. Linear specification Regress average commute time ( time_commuting ) on household income ( hh_income ).
Interpret the coefficient and comment on its statistical significance.
Q11. Log-linear specification Regress the log of average commute time on household income. Interpret the
coefficient and comment on its statistical significance.
Q12. Log-log specification Regress the log of average commute time on the log of household income. Interpret the
coefficient and comment on its statistical significance.
Multiple linear regression and indicator variables
Q13. Regress average commute time on household income and the share of the individuals in the household who
are non-white ethnicities ( hh_share_nonwhite ). Interpret the intercept and coefficient and comment on their
statistical significance. Also compare your results to Q10. Has anything changed?
Q14. Regress average commute time on the indicator variable for whether a household moved in the last year
( i_moved ). Interpret the intercept and coefficient and comment on their statistical significance.
Q15. Add the share of the household that represents a non-white ethnicity ( hh_share_nonwhite ) to the regression
in Q14. Note: Your outcome variable is still average household commute time, but you should now have two
explanatory variables. Interpret the intercept and coefficient and comment on their statistical significance.
Q16. Did adding this second explanatory variable change the coefficient of the first variable at all? What does that
Q17. Now add the interaction between your two explanatory variables in Q16 and re-run the regreation. (You
should have an intercept and three coefficients—the two variables plus their interaction.) Interpret the coefficient
on the interaction and comment on its statistical significance.
Q18. Did including the interaction change your understanding of the relationship between the variables? Explain.
Q19. Regress the indicator for whether the household has a smartphone ( i_smartphone ) on the household's
income ( hh_income ) and the share of the household's individuals who represent non-white ethnicities
( hh_share_nonwhite ). Interpret the intercept and coefficients. Comment on their statistical significance.
The bigger picture
Q20. In the last regression (Q19), should we be concerned about omitted-variable bias? Explain your answer and
provide an example of a potential omitted variable if you are concerned about omitted-variable bias.
Q21. Is R-squared a good measure of model performance? Explain your answer.
Q22. Define the term standard error.
Q23. What does our assumption of exogeneity require?
Q24. What does it mean for an estimator to be unbiased?
Q25. What does it mean for an estimator to be more efficient than another estimator?
Description of variables and names
Variable Description
fips County FIPS code
hh_size Household size (number of people)
hh_income Household total income in \$10,000
cost_housing Household's total reported cost of housing
n_vehicles Household's number of vehicles
hh_share_nonwhite Share of household members identifying as non-white ethnicities
i_renter Binary indicator for whether any household members are renters
i_moved Binary indicator for whether a household member moved in prior 1 year
i_foodstamp Binary indicator for whether any household member participates in foodstamps
i_smartphone Binary indicator for whether a household member owns a smartphone
i_internet Binary indicator for whether the household has access to the internet
time_commuting Average time spent commuting per day by each household member (minutes)
In general, I've tried to stick with a naming convention. Variables that begin with i_ denote binary indicatory
variables (taking on the value of 0 or 1). Variables that begin with n_ are numeric variables.