ECON 7202 - Advanced Econometrics V

North Terrace Campus - Semester 2 - 2014

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 heteroskedasticity (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
    Prerequisites A minimum of a Credit in ECON 3023 or ECON 3507 or ECON 7204 or equivalent
    Assessment Typically tutorial work, mid-semester exam & final exam
    Course Staff

    Course Coordinator: Professor Firmin Doko Tchatoka

    Location: Room 4.47, Nexus 10 Tower
    Telephone: 8313 1174
    Consultation time: 10 am - 11am on Wednesdays and by appointment

    Course Timetable

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

    Days Time Venue
    Mondays 3pm-5pm Lower Napier, LG09, Tutorial Room 7
    Wednesdays 1am-2:50pm Lower Napier, LG14, Eric Russell Seminar Room
    NB: Sometimes we will meet in the computer lab (10 Pulteney St., Room 2.20, on Mondays or Wednesdays). This will be announced in lectures and by email.
  • Learning Outcomes
    Course Learning Outcomes
    The outcomes of this course are:
    1 To learn various advanced econometric methods, estimation methods and related econometric theories
    2 To apply these methods to empirical data or develop new econometric theories
    3 Students are expected to be able to write a code in Matlab, Gauss, C++, etc, to estimate econometric models 
    4 Students are expected to be able to use Stata, Eviews, and etc, to estimate  econometric models using real world data
    5 Students are expected to be able to interpret time series models' estimates and analyze the results
    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)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2
    Skills of a high order in interpersonal understanding, teamwork and communication. 6
    A proficiency in the appropriate use of contemporary technologies. 2,3,4
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 2,5
  • Learning Resources
    Required Resources
    Lecture notes will be posted on MyUni before each lecture.

    J.M. Wooldridge Econometric Analysis of Cross Section and Panel Data 2nd Edition, MIT Press, 2010
     F. Hayashi Econometrics Princeton University Press, 2000
    Computer Software
    1 Matlab Available on the computers in Honours student room, PhD student room, and the computer lab (10 Pulteney St. 2.20 Computer Suite 1 and Computer Suite 3)
    2 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 ones 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 to the ITS to install Matlab in their machines.
    Recommended Resources
    A.C. Cameron and P.K. Travedi Microeconometrics: Methods and Applications Cambridge University Press, 2005
    J. Angrist and J-S. Pischke Mostly Harmless Econometrics Princeton University Press, 2009
    R. Davidson and J. G. MacKinnon Econometric Theory and Methods Oxford University Press, 2004
    W. H. Greene Econometric Analysis 5th Edition, Prentice Hall, 2003
    J. Hamilton Time Series Analysis Princeton University Press, 1994
    Online Learning
    1 E-mail Check your student email often as course-related announcements are communicated via email
    2 MyUni All the materials such as lecture notes, problem sets and their answer keys, Matlab manual, etc. will be posted on the MyUni course webpage,
    NB: Lecture notes will be put on the course webpage before each lecture. Students need to print out lecture notes and bring them to the class.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    1 Lecture notes
    2 Reading textbooks
    3 Just in time teaching (JiTT) assessment
    4 Problem solving and computer exercises
    NB: It is important for students to be able to apply what they learn in class to real world data by using computer programs such as Matlab, Gauss, C++, Stata and Eviews.

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

    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
    JiTT 4 hours/week
    Additional readings 1 hour/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
    Tentative Course Schedule (subject to changes)
    1 Review of the classical multivariate linear model: estimation, inference, and violation of basic assumptions
    2 Instrumental variables methods and related identification problems
    3 Nonlinear Models: nonlinear least squares (NLS), M-estimators, maximum likelihood (ML), generalized method of moments (GMM), minimum distance estimation
    4 Limited Dependent Variable Models
    5 Panel data methods
    6 Duration Models
    7 Introduction to Bootstrap Method
    8 Introduction to Unit-root Econometrics
    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
    The final mark for this course will be determined by:
    Assessment Task Task Type Due Weighting Learning Outcome
    Just in Time Teaching (JiTT): see Assessment Detail Readings

    Refer to course website on MyUni,

     10% 1-5
    Homework and Computer Exercises (HCE): see Assessment Detail Problem solving and computing Refer to course website on MyUni,  20% 1-5
    Midterm Exam: see Assessment Detail Formative, problem solving and computer exercises Refer to course website on MyUni, 20% 1-5
    Final Exam Formative, problem solving and computer exercises Refer to course website on MyUni, 50% 1-5
    Assessment Related Requirements
    Assessment Detail

    In the unit I plan to use the Just in Time Teaching (JITT) technique. You will be required to read some material before the relevant workshop and lecture. I will post the questions on MyUni. There will be three questions that will be covered in the following week’s lecture, workshops and labs. You will submit your answers by Sunday 5pm. It is important to bear in mind that while you will not be assessed on the content of your answers I will nevertheless use the JiTT assessments to form a question in the midterm and final exams. I will also form a view of the effort you are putting into being prepared for the following week’s class—I read your submissions before the Monday class. The mark here is an incentive to encourage you to participate rather than an assessment of the content


    Problem sets and computer exercises will be given regularly. Details (including submission dates) will be provided on MyUni and discussed with students in lectures. Late submission will be accepted only if accompanied by appropriate documentation, for example, a medical certificate. Each student must write and turn in her/his own homework to me right before the lecture begins in class on the due date. Students must write their name and student ID number on the cover sheet

    Midterm Exam

    2 hours test containing short answer questions. The date will be posted on MyUni and discussed with students in lectures. 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 70% (instead of 50%) of your total mark. Please note that, following University policy, dictionaries are not allowed in School of Economics exams. Students may NOT take any type of CALCULATOR to the exam

    Final Exam

    3 hours multi-part problem solving questions: will cover all the lectures, JiTT, Homework and Computer Exercises, and labs. Written sample answers will not be provided. Help with questions that you have made a genuine attempt to answer may be provided by your lecturer/tutor either on an individual basis or in a group revision session

    Refer to ASSESSMENT DETAIL. 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.

  • 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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