ECON 7001 - Econometrics PG

North Terrace Campus - Semester 1 - 2019

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.

  • General Course Information
    Course Details
    Course Code ECON 7001
    Course Econometrics PG
    Coordinating Unit 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
    Incompatible ECON 3502
    Assumed Knowledge Basic OLS (ordinary least squares) regression model, introductory statistics
    Restrictions Available to MFin&BusEc, MHlthEcPol, GCertAppEc, GCertIntEc, GDipIntEc, GDipAppEc & MAppEc students only
    Assessment Typically, tutorial participation, tests, assignments & a final exam
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    Dr. Nadezhda Baryshnikova
    Office location: Nexus 10, Level 4, Room 4.04
    Telephone: 8313 4821
    Office hours: to be advised on myUni

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    On successful completion of this course, students will be able to:

    1. Research with econometrics
    2. Explain econometrics concepts and results intuitively
    3. Derive econometric results mathematically
    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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
  • Learning Resources
    Required Resources
    Wooldridge J.M. (2016) Introductory Econometrics, 6th Edition. Cengage Learning.
    Recommended Resources
    Angrist J.D. and Pischke J.S. (2008) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
    Online Learning

    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, about 5 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 Summary
    Teaching & Learning Activities Related Learning Outcomes
    Class presentation 1,2
    Lectures 2,3
    Homework 2,3

    The 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
    f.  Matrices

    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 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.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Weighting Learning Outcomes
    Oral Presentation Group 10% 1,2
    Group Homeworks Group 10% 2,3
    Individual Homeworks Individual 30% 2,3
    Final Exam Individual 50% 2,3
    Total 100%

    Students are permitted to bring in an A4 size cheat sheet with both sides written for the midterm test and final exam.

    See myUni for due dates.

    Assessment Related Requirements
    1 - Attendance in class and tutorials is expected
    Assessment Detail

     1.Practice (not for grade): Homework will be posted each week. The students will be asked to form groups at the beginning of the course and do the exercises in these groups. Although homework is not marked, all students are expected to do this homework for practice!

    2. Graded Group Assignments (in groups) - 10%
    -There will be about 3 group homeworks throughout the course . The homeworks that will be graded will be announced on the course page in advance and will have a clearly marked due date. The tutor will randomly pick one question to mark.

    -No individual work will be accepted for this component. The group homework is submitted online.

    -Best 2 out of these 3 homeworks will be counted for assessment.

    -Because not all of these marks count for assessment (the "best of" policy), no special consideration will be given to students who do not submit the homwork (or submit it late) for medical, compassionate or any other reason.

    2.Individual Homework - 30%:

    -There will be 2 homework assignments to be submitted individually throughout the course.
    -The dates and submission guidelines will be announced on the course page. The tutor will randomly pick one question to mark in each of the two homeworks.
    -The best 1 of these 2 homeworks will be counted for assessment.
    -Because not all of these marks count for assessment (the "best of" policy), no special consideration will be given to students who do not submit the homwork (or submit it late) for medical, compassionate or any other reason.

    3. Presentations (in groups) - 10%

    -Students (in groups) will present published papers from established journals in tutorials in the second half of the semester.
    -Dates will be discussed and posted on myUni
    - This project is fully redeemable in that if students choose not to do this project or are unable to do this project or fail this project, the waiting of this project is added to the weighting of the final exam. No special consideration will be given to students who miss the project for medical, compassionate or any other reason.

    3. Final exam (50%)
    - The final test will be held in the lab during week 13 or 14. The dates will be announced on myUni in advance.
    -This exam covers the whole semester.
    -Please note that, following University policy, dictionaries are not allowed in School of Economics exams.
    -Graphics calculators or scientific calculators are allowed but are not required.
    -Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process. If we can’t read what you have written, we can’t give marks for it. Students are advised to use a black pen in the exam; write clearly, BEGIN each question ON A NEW PAGE and do not use white out or pencil.

    There is no extra work that can be done to redeem individual components of assessment during the semester, no matter the reason;
    Assessment marks prior to the final exam may be displayed on the course website. Students are encouraged to check their marks and notify the course coordinator of any discrepancies.

    Each assessment addresses University Graduate Attributes of deep discipline knowledge, critical thinking and problem solving skills, career and leadership readiness to achieve the Course Learning Outcomes 1, 2, and 3. In addition, group homework addressess University Graduate Attributes of teamwork and communication skills and self-awareness and emotional intelligence to achieve the Course Learning Outcomes 2 and 3.

    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  (and the Schedule to the Policy) for further details about eligibility and application forms.

    Submission of the assignments is required as per instructions on MyUni.
    Course Grading

    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  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.
  • Student Feedback

    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 ( 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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