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ECMT1020 Introduction to Econometrics
创建日期：2024-05-28 17:48:15
所属分类：经济Economics
标签： ECMT1020
Introduction to Econometrics

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ECMT1020 Introduction to Econometrics 

Assignment
Due: 11.59PM Friday 26 May 2023
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 assignment accounts for 15% of your final grade. There are 10 questions in
this assignment with 5 marks worth each, and the full mark of the assignment is 50.
Please attempt all questions.
2. This assignment entails the use of econometric models and statistical tools in eco-
nomic application. You will use statistical software, Stata, to analyze the educa-
tional attainment and wage equations data.
3. Please save your answers in a pdf file1 named 123456789.pdf where 123456789 is
your 9-digit SID. Do not put your name in your work or anywhere in your submis-
sion. Do not include a cover sheet.
4. The dataset you will use is in the Excel spreadsheet EAWE#.xlsx, where # is the
last digit of your University of Sydney SID. Please use your assigned data set
to answer the questions and write your data set number on the front page of your
work. Using the wrong data set will be reviewed as a potential case of Academic
Dishonesty.
5. Answer all the questions. Show all numerical answers to 2 decimal places if neces-
sary. When you are asked to ‘perform a test’, you should write explicitly the null
hypothesis of the test, and state clearly how you make testing decisions and make
conclusions. Please carry out all tests using a 5% level of significance.
6. You should include Stata procedures and outputs in your answers, and your own
interpretations and explanations are necessary for earning marks. Please type your
answer in a document. We do not accept handwritten solutions.
7. When answering the questions, please keep your statements concise as well as ac-
curate. Excessively long responses indicate a lack of understanding and will be
penalized accordingly.
8. Submit one pdf file through Turnitin under the Canvas module ‘Assignment’. Late
submission is subject to a penalty of 5% of total 50 marks, which is 2.5 marks, per
day. Work submitted more than 10 days after the due date will receive a mark of
zero. There are in accordance with 7A in the University Assessment Procedures
2011.
1You may type your answers in a Word document and then save it as a pdf file.
1
Data Description
You will use a subset consisting of 500 observations of Educational Attainment and Wage
Equations (EAWE) dataset to answer the questions. The description of the data set
and contained variables can be found in Appendix B on p.565–569 of the textbook (also
provided in a separate pdf file).
In particular, note that EXP and TENURE in your dataset are, respectively, the
number of years at work and the number of years spent working with the current employer.
We define a new variable PREVEXP = EXP−TENURE. PREVEXP, thus defined, is the
total work experience with previous employers, and will be used in some of the questions.
We use LGEARN to denote the logarithm of EARNINGS.
Questions
1. Fit an educational attainment function using your data set. Regress S on ASVABC,
SM and SF, and interpret the regression results. Perform t tests on the coefficients
of the variables in the education attainment function.
2. Use the Box and Cox procedure (Steps 1–3) described on p. 211 of the textbook to
evaluate whether the dependent variable of a wage regression of EARNINGS on S
and EXP should be linear or logarithmic based on your dataset.
3. Following up on the previous question, now define demeaned variables
S∗ = S − S and EXP∗ = EXP− EXP,
where S and EXP denote the sample mean of S and EXP. Regress EARNINGS or
LGEARN, depending on your result in the previous question, on S∗ and EXP∗ and
interpret the regression output. In particular, how is the interpretation of the two
slope coefficients in this regression different from the regression where regressors are
S and EXP instead?
4. Consider the following two regressions:
(a) Regress LGEARN on S and PREVEXP ;
(b) Regress LGEARN on S, TENURE and PREVEXP.
Before running any regressions in your software, what is your expectation of the
relative magnitude of the coefficients of PREVEXP in the two fitted regressions?
Explain why you expect so. Then, run these two regressions in Stata and see if the
result confirms your expectation.
5. Explain how you could get the same OLS estimate of the coefficient of PREVEXP
in the multiple regression in Question 4(a) using “purged regressions”. Implement
your procedure in Stata and show the results are matched.
6. Consider a regression of LGEARN on S, EXP, TENURE, PREVEXP, ASVABC,
ETHBLACK and ETHHISP. Can you run this regression in Stata? If yes, please
report your output. If not, please explain why this is the case.
7. Regress LGEARN on S, PREVEXP, TENURE, ASVABC, ETHBLACK and ETH-
HISP. Explain how you would conduct an F test for testing the coefficients in front
of PREVEXP and TENURE being equal. Please perform the test and interpret
your result.
2
8. Regress LGEARN on S, EXP, TENURE, ASVABC, ETHBLACK and ETHHISP.
Perform a t test on the coefficient of TENURE. Please explain why such a t test is
a test of the same restriction described in Question 7. Verify that the same result
is obtained from the previous F test and the t test here.
9. How do you interpret the coefficients of ETHBLACK and ETHHISP in the fitted
regression in Question 8?
10. Add an intercept dummy MALE and a slope dummy defined as the product of
MALE and S in the regression in Question 8, and then run the regression again.
Interpret the coefficients before these two dummy variables. Is the effect of education
on earnings different for males and females?
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