# Introduction to Econometrics Coursework

Introduction to Econometrics Coursework

2020/21

Coursework Questions

Instructions for Introduction to Econometrics Coursework Questions

The deadline for this Coursework is Friday 20th of November 2020, 10am.

Upload your answers in a single file via Turnitin using the link on KEATS.

Include at the end of the file all the Stata commands you used to answer the questions.

Keep your answers short, approx. 30-70 words and no more than 100 words for any

question. Maximum 700 words overall (excluding Stata tables and commands).

The data for Introduction to Econometrics Coursework Questions

In this coursework exercise you will investigate the relationship between a person’s labour

earnings and age, education, gender and a few other characteristics. The file cwdata.dta

contains data for full-time workers in the US. The data are from the US National Health

Interview Survey. The sample is chosen randomly from the whole population of the US.

The variables included in cwdata.dta are :

earnings Annual labour earnings (expressed in USD per year)

age Age in years

educ Years of schooling

woman Gender (0 man, 1 woman)

married Marital status ( 0 unmarried, 1 married)

region Region (1 Northeast, 2 Midwest, 3 South, 4 West)

manager Executive / managerial position (0 no, 1 yes)

height Height without shoes (in inches)

Questionsfor Introduction to Econometrics Coursework Questions

1. Run a regression of annual earnings (earnings) on age (age). How do you interpret

the intercept and slope coefficients? (10 marks)

2. Run a regression of earnings on age, gender (woman), and education (educ). What is

the effect of age on yearly earnings according to this regression? (10 marks)

3. Are the results from the regression in (1) substantively different from the results in

(2) regarding the effects of age on annual earnings? Does the regression in (1) seem

to suffer from omitted variable bias? (20 marks)

[Hint: explain the direction of this bias.]

4. Include now some of the other control variables in the regression: regress earnings

on age, age2, woman, educ, married, and manager. If age increases from 35 to 36

years, how are annual earnings expected to change? (20 marks)

[Hint: create a new variable using: gen age2=age^2. Alternatively, you can do this

automatically in Stata using the operator c.age##c.age.]

1

5. Run the same regression as in (4) but using the logarithm of earnings: regress log of

earnings on age, age2, woman, educ, married, and manager. What is the effect of

an additional year of schooling on earnings according to this regression? (15 marks)

[Hint: create the new variable log earnings using: gen lnearn=ln(earnings)]

6. Run the same regression as in (5) but include also the interaction term woman x educ.

What does the coefficient of this interaction term measure? (15 marks)

[Hint: you can do this automatically in Stata using the operator woman##c.educ.]

7. Include now height in the regression from question (5). Does the height of a person

appear to be a determinant of earnings? Is the effect of height on earnings different

for women than for men? Specify and estimate a regression (or two) that you can

use to answer this question. (10 marks)

Place an order with us on https://assignmentsproficient.com/order/ and expect the best grades and help from qualified writers