ECON 7223 - Time Series Econometrics IV

North Terrace Campus - Semester 2 - 2021

The aim of this course is to study time series methods in econometrics. Students are expected to have knowledge in statistics and Level IV econometrics or equivalent. Topics typically include stationarity, unit roots, autoregressive moving average (ARMA), forecasting, maximum likelihood estimation (MLE), vector autoregression (VAR), structural vector autoregression (SVAR), and co-integration. The emphasis is on understanding the methods and applying them to real-world data.

  • General Course Information
    Course Details
    Course Code ECON 7223
    Course Time Series Econometrics IV
    Coordinating Unit School of 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 ECON 7204 or equivalent
    Incompatible ECON 4013
    Course Description The aim of this course is to study time series methods in econometrics. Students are expected to have knowledge in statistics and Level IV econometrics or equivalent. Topics typically include stationarity, unit roots, autoregressive moving average (ARMA), forecasting, maximum likelihood estimation (MLE), vector autoregression (VAR), structural vector autoregression (SVAR), and co-integration. The emphasis is on understanding the methods and applying them to real-world data.
    Course Staff

    Course Coordinator: Professor Firmin Doko Tchatoka

    Location: Room 4.47, Nexus 10 Tower
    Telephone: 8313 1174

    Consultation time: TBA
    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. Use various advanced time series econometric methods, estimation methods and related econometric theories.
    2. Apply these methods to empirical data or develop new time series econometric theories.
    3. Use a number of specialist software such as Matlab, Gauss, C++, Stata and Eviews.
    4. 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)
    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
  • Learning Resources
    Required Resources
    Lecture notes will be posted on MyUni before each lecture.

    Stata Manual Stata Time Series Manual Published by Stata Press
    J. Hamilton  Time Series Analysis Princeton: Princeton University Press, 1994
    P. J. Brockwell and R. A. Davis Time Series: Theory and Methods 2nd edition. New York: Springer-Verlag, 1991
    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 ITS to install Matlab on their machines.
    Recommended Resources
    Robert H. Shumway and David S. Stoffer Time Series Analysis and Its Applications With R Examples 2nd edition. Springer, 2006
    F. Hayashi Econometrics  Princeton University Press, 2000
    John Y. Campbell, Andrew W. Lo, and A. Craig Mackinlay The Econometrics of Financial Markets Princeton University Press, 1997
    Online Learning
    1 Email  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 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.

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

    Teaching & Learning Activities Personal Study Hours
    (Outside Your Regular Classes)
    Lecture notes 2 hours/week
    Additional readings and empirical project 4 hours/week
    Problem solving and computer exercises 3 hours/week
    NB: The above guide is for private study, that is, study outside of your regular classes.
    Learning Activities Summary
    Teaching & Learning Activities Related Learning Outcomes
    Lecture notes 1,2,4
    Additional readings and empirical project 1,2,3
    Problem solving and computer exercises 1,2,3

    TENTATIVE LECTURE SCHEDULE (subject to changes)
    1 Univariate time series: Unit root, testing for unit root, stationarity
    2 Autoregressive and moving average models (ARMA)
    3 Vector Autoregression and Structural Vector Autoregression Models
    4 Cointegration and Error Correction
    5 Identification of Structural Vector Autoregression Models: External Instruments and Sign Restrictions
    6 Panel  Vector Autoregression Models
    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
    Homework: see Assessment Detail  Problem solving  and formative Refer to course website on MyUni, 20% 2,3,4
    Computer Exercises  and Group empirical projects Computer and formative Refer to course website on MyUni,  40% 2,3,4
    Final Examination Formative, problem solving and computer exercises Refer to course website on MyUni, 40% 1,2,4
    Total 100%
    Assessment Related Requirements
    Assessment Detail
    1. Homework, Computer Exercises, and group projects

    Problem sets, computer exercises and group projects 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. Students must write their name and student ID number on the cover sheet of their work.

    2. Final Exam

    3 hours multi-part problem solving questions: will cover all the lectures,  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.

    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
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