ECON 7202 - Advanced Econometrics V

North Terrace Campus - Semester 2 - 2020

In this course we study advanced micro and time series econometrics topics that are not covered in Econometrics IV and Advanced Time Series Econometrics IV or those topics discussed in these two courses in more detail. Topics can include bootstrap, generalized method of moment (GMM), empirical likelihood (EL), instrument variables (IV) estimation, maximum likelihood estimation (MLE), panel data methods (basic models, dynamic panel model, panel model with limited dependent variable), limited dependent variable models, sample selection corrections, duration model, autoregressive conditional heteroscedasticity (ARCH), generalized ARCH (GARCH), Kalman filter, regime switching model, stochastic calculus, diffusion models, and financial economics.

  • General Course Information
    Course Details
    Course Code ECON 7202
    Course Advanced Econometrics V
    Coordinating Unit Economics
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites A minimum of a Credit in ECON 3502 or ECON 7001 or ECON 7204 or equivalent.
    Assumed Knowledge Basic matrix algebra, basic Matlab and STATA
    Assessment Typically homework & final exam; sometimes paper and presentations
    Course Staff

    Course Coordinator: Dr Terence Cheng

    Location: Room 4.06, Nexus 10 Tower
    Telephone: 8313 1175

    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 Learn various advanced econometric methods, estimation methods and related econometric theories
    2 Apply these methods to data or econometric modelling techniques
    3 Write a code in Stata to estimate econometric models and replicate results from published econometrics research
    4 Use Stata, Eviews, and etc, to estimate  econometric models using real world data
    5 Interpret econometric estimates, analyse the results and critically evaluate published econometric research.
    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, 5
    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, 4, 5
    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
    4, 5
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • Learning Resources
    Required Resources

    1 A.C Cameron and P.K. Trivedi Microeconometrics: Methods and Applications Cambridge University Press, 2005
    2  J. Angrist and J.S. Pischke Mostly Harmless Econometrics Princeton University Press, 2009
    Computer Software
    1 Stata Available on the computers in Honours student room, PhD student room, and the computer lab (10 Pulteney St. 2.20 Computer Suite 3 only)
    NB: Students are encouraged to use software other than the programs listed here. However, they must ensure that the software is appropriate for their project. Students who use computers connected to the University network can make a request with ITS to install Matlab on their machines.
    Recommended Resources
    i) J.M. Wooldridge  Econometric Analysis of Cross Section and Panel Data 2nd Edition, MIT Press, 2010
    ii) A.C. Cameron and P.K. Trivedi Microeconometrics Using Stata Revised Edition Stata Press, College Station: TX, 2009
    iii) Train, K.E. Discrete Choice Methods with Simulation Cambridge University Press, 2003
    iv) W. H. Greene Econometric Analysis 7th Edition, Pearson
    5 & 6 Ed. Prentice Hall, 2003
    v) R. Winkelmann and S. Boes Analysis of Microdata 2nd Edition, Springer, 2009
    vi) Gould W., J. Pitblado and W. Sribney Maximum Likelihood Estimation with Stata Third Edition, Stata Press, College Station: TX, 2006
    Online Learning
    1 E-mail Check your student email often as course-related announcements are communicated via email
    2 MyUni Course materials will be posted on the MyUni course webpage,

  • Learning & Teaching Activities
    Learning & Teaching Modes
    1 Lecture slides
    2 Tutorial exercises
    3 Computer exercises and program codes
    4 Textbooks
    5 Journal articles


    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    All students in this course are expected to attend all lectures, workshops and labs throughout the semester.

    Lecture notes 2 hours/week
    Additional readings 2 hours/week
    Problem solving and computer exercises 2 hours/week

    NB: The above guide is for private study, that is, study outside of your regular classes.

    Learning Activities Summary

    a) Causal Models and Treatment Evaluation
    b) Models for Cross-Section Data: Discrete and Limited Dependent Variables; Mixture Models.
    c) Models for Panel Data: Linear and Dynamic Panels; Non-Linear Panels. Missing Data
    d) Maximum Likelihood (ML) using Stata; Simulation-Based ML Estimation
    e) Programming and Data Management using Stata
    Specific Course Requirements
    Small Group Discovery Experience
  • 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 Due Weighting Learning Outcome
    Two in-class presentations Formative and Summative Friday, Week 3
    Friday, Week 9
    20% 1, 2, 3, 4
    Research project Formative and Summative Week 12 40% 1, 2, 4, 5
    Final exam Formative and Summative Exam period 40% 1, 2, 4, 5
    Assessment Related Requirements
    Assessment Detail
    Homework and computer
    Assignments will be made available on MyUni and distributed in the tutorials the teaching week before they are due. They need to be handed in at the beginning of the lecture the week they are due. Late assignments will be accepted only if accompanied by appropriate documentation. Assignments consist of a mix of paper-and-pencil and software exercises, and would involve reading a journal article from the literature.
    Midterm Exam Mid-term examination containing short answer and problems/computational questions. There will be no supplementary exam for the midterm exam. If you miss this exam and you provide a medical certificate or compassionate reasons, your final exam will account for 90% (instead of 60%) of your total mark. The date will be posted on MyUni and discussed with students in lectures.
    Final Exam Final examination containing short answer and problems/computational questions. The date will be posted on MyUni and discussed with students in lectures.
    After being marked, generally, the assessment will be returned to students in class about a week after submission.
    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 (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.
  • 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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