ECON 4013 - Time Series Econometrics IV (H)

North Terrace Campus - Semester 2 - 2023

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 4013
    Course Time Series Econometrics IV (H)
    Coordinating Unit Economics
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 4014
    Incompatible ECON 7223
    Restrictions Available only to students enrolled in the Bachelor of Economics (Honours) program
    Assessment Typically homework problem sets, Just in time teaching (JiTT), empirical project, mid-term test & final exam.
    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 Learn 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 Write a code in Matlab, Gauss, C++, etc.
    4 Use Stata, Eviews, and etc, to estimate time series econometric models using real world data
    5 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)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    Lecture notes will be posted on MyUni before each lecture.

    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 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 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.
    Lecture notes 2 hours/week
    Additional readings and empirical project 3 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
    Tentative Course 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
  • 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
    Problem sets and Computer: see Assessment Detail  Formative & Summative
    Problem solving and computing

    20% 1-5
    Empirical projects: see Assessment Detail  Formative & Summative reading and  computing
    40% 1-5
    Final Exam Summative problem solving and computer exercises
    40% 1-5
    Assessment Related Requirements
    Assessment Detail
    1. Problem sets and Computing

    Up to fourpProblem sets and computing exercises will be given to you through the semester. 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 lecture begins in class on the due date. Students must write their name and student ID number on the cover sheet.

    3. Empirical Projects

    A strong emphasis of the course will be the application of the methologies learned to real macroeconomic policy  settings. 
    Students will be given up to four applied project to complete throughout the semester.  The maximum length of  each project will be given on MyUni along with specific guidelines.   


    4. Final Exam

    2 hours multi-part problem solving questions: will cover all the lectures, problem sets  and computer  labs. Written sample of answers will not be provided.  
    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:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    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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