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ECMT1020 Introduction to Econometrics
创建日期：2024-05-06 16:23:48
所属分类：经济Economics
标签： ECMT1020
Introduction to Econometrics

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ECMT1020 Introduction to Econometrics 

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. There are 10 questions in this assignment each worth 3 marks. The assignment has
a maximum of 30 marks and accounts for 15% of your final grade.
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. The assignment is anonymously marked. 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 submission. Do NOT include a cover sheet.
4. Your assigned data set is 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. 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 30 marks, which is 1.5 marks, per
calendar 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 can write 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) data set 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).
Questions
1. Fit a wage equation by regressing EARNINGS on EXP, and perform t tests on the
intercept and slope coefficient. Then, perform an F test on the explanatory power
of the model and explain the relationship between this F test and the t test on the
slope coefficient.
2. Fit another wage equation by regressing EARNINGS on S and EXP, and interpret
all the parameter estimates (with attention paid to their significance). Compare the
slope coefficient of EXP in this equation with the one obtained in Question 1 and
interpret the difference.
3. Perform an F test of the explanatory power of the equation you obtained in Ques-
tion 2. Calculate the F statistic using R2 of the fitted regression and verify it is the
same as the F statistic in your Stata output.
4. Regress the logarithm of EARNINGS on S and EXP. Carefully interpret the re-
gression results, perform t tests on the coefficients and F test of the explanatory
power of the model.
5. 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 (like in Question 2) or logarithmic (like in Question 4).
6. Regress S on ASVABC, MALE, SM, SF, ETHHISP and ETHBLACK. Use your
results to answer the question: Does ethnicity affect educational attainment, and if
yes, how?
7. Redo Question 6 making ETHBLACK the reference category. What are the impacts
of change of reference on the interpretation of the coefficients and the statistical tests
(t tests of the coefficients and F test of the model)?
8. Define a slope dummy variable as the product of MALE and ASVABC. Regress the
S on ASVABC, MALE, SM, SF, ETHHISP, ETHBLACK, and the slope dummy
variable. First, explain what this regression with slope dummy variable can be
useful for. Second, what are your findings based on your regression results?
9. The variable TENURE in your data set is the number of years spent working with
the current employer. So
PREVEXP = EXP− TENURE
is total work experience with previous employers. Now, run the following regression
LGEARN =β1 + β2S + β3PREVEXP + β4TENURE
+ β5ASVABC + β6MALE + β7ETHBLACK + β8ETHHIP + u,
2
where LGEARN is the logarithm of EARNINGS. Suppose we want to understand
whether work experience with previous employers as valuable as experience with the
current employer, how do you formulate a hypothesis test to answer this questions?
Please write explicitly the null and alternative hypotheses, what test you would
perform and how to conduct the test. What’s your conclusion using your data set?
10. Fit a wage equation by regressing the logarithm of EARNINGS on S, EXP and
MALE. Perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension.
Why heteroskedasticity is a concern when we conduct the regression analysis?
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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