ECMT1020 Introduction to Econometrics Semester 1, 2025
Group Assignment
Due: 11.59pm Friday 23 May 2024
Academic Dishonesty and Plagiarism
Academic honesty is a core value of the University, and all students are required to act honestly,
ethically and with integrity. The consequences of engaging in plagiarism and academic dishonesty,
along with the process by which they are determined and applied, are set out in the Academic
Honesty in Coursework Policy 2015. Under the same policy, as the unit coordinator, I must report
any suspected plagiarism or academic dishonesty.
Instructions
1. This group assignment counts for 10% of your final grade. You may form a group
with any student enrolled in this unit (not necessarily from your tutorial), with a
maximum group size of two. The assignment will be marked based on the final
group submission, and group members will receive the same mark.
2. Once you have formed a group, make sure to register your group on Canvas (instruc-
tions are provided on this Canvas page). Only one group member needs to submit
the assignment on behalf of the group. Individual submissions are also permitted.
If you complete the assignment on your own, there is no need to join a group on
Canvas.
3. The assignment consists of 12 questions and is worth a total of 40 marks. Marks
allocated to each question are indicated. You are expected to attempt all questions.
4. The dataset assigned to your group is provided in the Excel file EAWE#.xlsx,
where # corresponds to the last digit of the sum of the last digits of the University
of Sydney SIDs of your group members. For example, if student A and student B
form a group, and student A’s SID ends in 3 and student B’s SID ends in 8, then
3 + 8 = 11, and the last digit of 11 is 1. Therefore, this group should use dataset
EAWE1.xlsx.
5. You must use your assigned dataset to answer the questions. Clearly indicate your
dataset number and the SIDs of both group members on the front page of your
submission. Use of the incorrect dataset may be treated as a case of Academic
Dishonesty.
6. Round all numerical answers to three decimal places if applicable. When asked
to “report the estimation results”, include both the relevant Stata commands and
output tables in your answer (you may insert the screenshots into your document).
A separate Stata do-file is not required.
7. Your answers must be typed—handwritten submissions will not be accepted. En-
sure your answers are clear and concise. Lengthy responses that lack focus or
understanding will be penalized.
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8. Submit a single pdf file1 named EAWE# SID1 SID2.pdf where # is your assigned
dataset number, and SID1 and SID2 are the 9-digit SIDs of the group members. Do
not include your names or a cover sheet.
9. The submission is via a file upload under the Canvas module “Assignment”. You
may submit up to two times, and only the most recent submission will be counted.
Late submission will incur a penalty of 5% of the total 40 marks (i.e., 2 marks) per
calendar day. Submissions more than 10 calendar days late will receive a mark of
zero. These rules follow Section 7A of the University Assessment Procedures 2011.
10. Regarding the use of AI tools: I will not impose unenforceable restrictions. You
are permitted to use AI to assist with this assignment; however, you must clearly
declare any such use and explain how it contributed to your work.
Questions
1. (3 marks) Create the following two scatter plots using the variables in your dataset:
(i) EARNINGS (hourly earning in dollars) on the y-axis and S (education: years
of schooling) on the x-axis.
(ii) EARNINGS (hourly earning in dollars) on the y-axis and EXP (work experi-
ence: years spent working after leaving full-time education) on the x-axis.
Ensure that each plot includes clearly labeled axes. What are the observed values
of S in your dataset?
2. (2 marks) Estimate a regression model with EARNINGS as the dependent variable
and EXP as the only explanatory variable. Report the estimation results and write
down the fitted regression equation.
3. (5 marks) Is the slope coefficient in the fitted model from Question 2 significantly
different from zero at the 10% significance level? Explain how you can reach your
conclusion based on the regression output. In your explanation, clearly state the
null and alternative hypotheses, and describe the three ways to make your decision
based on
(i) the test statistic,
(ii) the p-value of the test, or
(iii) the confidence interval for the slope coefficient.
4. (4 marks) What are the confidence intervals for the intercept and slope coefficients
in your regression output from Question 2? Explain how these confidence intervals
are constructed. What are the center values of these confidence intervals?
5. (4 marks) Estimate another regression model with EARNINGS as the dependent
variable and both EXP and S as the explanatory variables. Report the estima-
tion results and write down the fitted regression equation. Carefully interpret the
estimated intercept and slope coefficients.
1You may type your answers in Word and export the file as a pdf.
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6. (3 marks) Is the slope coefficient on EXP in the fitted model from Question 2 smaller
or larger than the slope coefficient on EXP in the fitted model from Question 5?
Provide a reasonable explanation for this difference.
7. (4 marks) What are the sample means of S and EXP in your dataset?2 Define
two new variables, SDEV and EXPDEV, as deviations of S and EXP from their
respective sample means:
SDEV = S− S and EXPDEV = EXP− EXP.
Estimate a regression model with EARNINGS as the dependent variable and both
EXPDEV and SDEV as explanatory variables. Report the estimation results and
write down the fitted regression equation. Interpret the estimated slope coefficients.
8. (2 marks) How does the regression with “demeaned” explanatory variables in Ques-
tion 7 improve the interpretation of the fitted intercept compared to the regression
in Question 5?
9. (3 marks) The variable TENURE in your dataset represents the number of years
an individual has spent working with the current employer. Define a new variable
PREVEXP = EXP− TENURE
to represent prior work experience with previous employers. Also, define LGEARN
as the natural logarithm of EARNINGS. Estimate a regression model with LGEARN
as the dependent variable and PREVEXP, TENURE, and S as explanatory vari-
ables. Report the estimation results and interpret each of the three slope coefficients.
10. (2 marks) What would happen if you include the variable EXP in the regression
model from Question 9? Explain why this happens.
11. (2 marks) Based on the fitted relationship between LGEARN and the explanatory
variables in Question 9, derive the corresponding fitted relationship between the
original dependent variable EARNINGS and the variables PREVEXP, TENURE
and S.
12. (6 marks) In your dataset, the variable HOURS represents the number of hours
worked per week (a proxy for labor supply) and MARRIED is a dummy variable
which takes value 1 if the individual is married, and 0 otherwise. Estimate the
following regression model:
LGHOURS = β1 + β2LGEARN+ β3(MARRIED× LGEARN) + β4S+ β5EXP+ u,
where LGHOURS is the natural logarithm of HOURS, and the other variables are
as previously defined. Report the estimation results and carefully interpret the
estimates of β2 and β3. Additionally, write out the fitted relationships between
HOURS and the variables EARNINGS, S, and EXP separately for married and
unmarried individuals.
2Use the commands .sum S and .sum EXP in Stata to view summary statistics for S and EXP.
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