ECON 7001 - Econometrics PG
North Terrace Campus - Semester 1 - 2016
General Course Information
Course Code ECON 7001 Course Econometrics PG Coordinating Unit School of Economics Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites ECON 7051 or equivalent Assumed Knowledge Basic OLS (ordinary least squares) regression model, introductory statistics Restrictions Available to MFin&BusEc, GradCertEc, GradCertIntEc, GradDipIntEc, GradDipAppEc, MAppEc(Int), MAppEc, MAppEc(PubPolicy) & MEc(Course) students only Course Description The course focuses on the estimation, inference and identification of linear models. We will discuss the issues and challenges of linear regression models, how to interpret the results of these models, and apply econometrics can be applied to study real-world problems. The topics to be covered in the course include estimation issues such as model misspecification, measurement errors endogenous regressors, as well as instrumental variable regressions, panel data approaches, and econometric analysis using matrices. STATA, a standard software for econometric and statistical analysis, will be used throughout the course. Practical applications of the course will be discussed during the fortnightly seminar.
Course Coordinator: Dr Nicholas SimDr Nicholas Sim
Office location: Nexus 10, Level 4, Room 4.46
Telephone: 8313 4927
Office hours: Wednesday 4-5pm
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesThe course aims to achieve the following outcomes. It will provide students:
1. with knowledge on the fundamentals of econometric theory and its applications
2. with knowledge and proficiency on the use of statistical packages for econometric and statistical analysis
3. with the ability to conduct independent data analysis and inquiry using the tools of statistics and econometrics
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
Required ResourcesWooldridge J.M. (2016) Introductory Econometrics, 6th Edition. Cengage Learning.
Recommended ResourcesAngrist J.D. and Pischke J.S. (2008) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
MyUni Course WebPage provides lecture notes, computer lecture notes, homework questions, solutions and practice exams. Please check this page frequently for important announcements and corrections.
Learning & Teaching Activities
Learning & Teaching Modes
Classes will meet twice per week, for a 2-hour lecture and a 1- hour tutorial. Students are expected to be present for all lectures and actively participate in all tutorial activities. The lecturer will hold office hours except for breaks and holidays, with additional hours held by the tutor.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The standard postgraduate workload for a full-time student is 48 hours per week which equates to 12 hours per 3 unit course. This course has two hours of lectures and one hour of tutorials each week, which means that students should undertake nine hours of self-study each week of the teaching term.
Weekly homework assignments are issued, two of which are to be submitted and graded at the discretion of the lecturer. All students may be asked to present their solutions during each tutorial session.
Learning Activities SummaryThe tentative outline of the course (subject to change) is:
1. Review of Mathematical Tools, Probability Distributions and Statistical Inference (Wooldridge: Appendices A-C)
a. Basic mathematical tools
b. Probability distribution
c. Point and interval estimation
d. Large sample properties of estimators
e. Hypothesis testing and confidence intervalslg
2. Linear Regression Analysis (Wooldridge: Chapters 1-3)
a. Economic Data
b. Simple linear regression and ordinary least squares (OLS) estimation
c. Multiple linear regression
d. The properties, expected value and the variance of the OLS estimator
3. Issues in Multiple Regression Analysis (Wooldridge: Chapters 4-7)
a. Inference and hypothesis testing
b. Large sample properties of the OLS estimator
c. Other functional form
d. Goodness of fit
e. Qualitative data (Binary variables)
4. Heteroskedasticity (Wooldridge: Chapter 8)
a. Heteroskedasticity-robust inference
b. Testing for heteroskedasticity
c. Weighted least squares estimation
5. Specification and Data Issues (Wooldridge: Chapter 9)
a. Functional form misspecification
b. Proxy variables
c. Measurement errors
Subject to time availability, one or more of the following topics will be covered:
6. Panel Data (Wooldridge: Chapters 13-14)
a. Fixed effects estimation
b. Random effects estimation
7. Limited Dependent Variable Models and Sample Selection Corrections (Wooldridge: Chapter 17)
a. Logit and probit models
b. Tobit models
c. Poisson regression model
d. Models with censored and truncated data
e. Sample selection
8. Instrumental Variables Estimation and Simultaneous Equations Model (Wooldridge: Chapters 15-16)
a. Instrumental variables
b. Two-state least squares estimation
c. Simultaneity bias in OLS
Specific Course Requirements
Homework completion may require access to STATA. If you do not have STATA at home, you may use the computer labs on campus. Please refer to http://www.adelaide.edu.au/its/student_support/labs/ for further details.
For course related questions, students are encouraged to utilise the designated office hours of the lecturer and the tutor. Questions over the telephone are strongly discouraged.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Students will be assessed based on the following:
Homework assignments 10%
Midterm test 20%
Term paper and presentation 30%
Final exam 40%
Unless instructed otherwise, students are permitted to bring in an A4 size cheat sheet with both sides written for the midterm test and final exam.
Assessment Related Requirements1 - Attendance in class and tutorials is required.
2 - To gain a pass, a mark of at least 45% must be obtained on the final test as well as a total of at least 50% overall.
Homework will be posted each week. The lecturer will choose two of the weekly homework assignments to be submitted and graded. For homework to be turned in, missed or late submissions will not be accepted and will be graded 0.
The final test will be in the lab during week 13 or 14. The dates will be announced in advance on myUni.
Supplementary test or examination will not be given to replace the missed ones.
Unless there are valid reasons and documentations, missed test or examination will be graded 0. Please refer to the Modified Arrangements for Coursework Assessment Policy http://www.adelaide.edu.au/policies/3303 (and the Schedule to the Policy) for further details about eligibility and application forms.
SubmissionSubmission of the assignments is required as per instructions on MyUni.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.Additional Assessment
If a student receives 45-49 for their final mark for the course they will automatically be granted an additional assessment. This will most likely be in the form of a new exam (Additional Assessment) and will have the same weight as the original exam unless an alternative requirement (for example a hurdle requirement) is stated in this semester’s Course Outline. If, after replacing the original exam mark with the new exam mark, it is calculated that the student has passed the course, they will receive 50 Pass as their final result for the course (no higher) but if the calculation totals less than 50, their grade will be Fail and the higher of the original mark or the mark following the Additional Assessment will be recorded as the final result.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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