ECON 4013 - Advanced Time Series Econometrics IV (H)

North Terrace Campus - Semester 2 - 2017

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 Advanced 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 or equivalent
    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)
    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
    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
    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 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.

    All students in this course are expected to attend all lectures, workshops and labs throughout the semester.
    Lecture notes 2 hours/week
    JiTT 3 hours/ week
    Additional readings and empirical project 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
    Tentative Course Schedule (subject to changes)
    1 Introduction to Time Series
    2 Stochastic Processes
    3 Univariate Times Series Models: Estimation and Inference
    4 Predictions/Forecasting
    5 Non-stationary Univariate Time Series Models
    6 Multivariate Time Series: Vector Autoregressive Models
    7 Cointegration and Error Correction
    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 MyUni

      10% 1-5
    Homework and Computer Exercises: see Assessment Detail  Problem solving and computing Refer to MyUni 15% 1-5
    Midterm Exam: see Assessment Detail  Formative, problem solving and computer exercises Refer to MyUni 15% 1-5
    Empirical project: see Assessment Detail  Formative, reading and  computing Refer to MyUni 20% 1-5
    Final Exam Formative, problem solving and computer exercises Refer to MyUni 40% 1-5
    Assessment Related Requirements
    Assessment Detail
    1. Just in Time Teaching (JiTT) 

    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 Saturday 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 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.

    2. Homework and Computer Exercises

    Problem sets and computer exercises will be given to you fortnightly. 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. Midterm Exam

    1 h 30 min 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 55% (instead of 40%) 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.

    4. Empirical Project

    Students must complete an applied time series econometrics study of an economic or financial relationship and answer a research question that they pose. The maximum length of the final version of the project is 15 pages + references + appendix. Students must replicate one of the following papers: 

    1) Anthony D. Hall, Heather M. Anderson and Clive W. J. Granger, 1992, A Cointegration Analysis of Treasury Bill Yields. 
    The Review of Economics and Statistics, Vol. 74, No. 1, pp. 116-126.

    2) Mardi Dungey, and Adrian Pagan, 2009, Extending a SVAR Model of the Australian Economy. The Economic Record,
    The Economic Society of Australia, vol. 85(268), pages 1-20, 03.


    The project is divided in three parts: A, B, and C. 

    Project Part A: Must contain the abstract and data description


    1. Clearly state the question that you will be investigating. Do not repeat the abstract from the source paper.
    2. Provide the source of the data or the name of the database that you are planning to use.
    3. Speculate what type of results you would expect to get in answer to your stated question.
    Expected length: 1 page

    Data Description: 

    4. Type of data (e.g. panel, time series, cross sectional, pooled cross sectional, etc.).
    5. Frequency.
    6. Dimensions of your data.
    7. List variables that you will be using for your project.
    8. Provide 5-point summary for all variables used in your analysis.
    9. Graph your data and interpret the results (stationarity, seasonality, trends, structural breaks…).
    Expected length: 3-4 pages

    Project Part B: Residual Analysis

    In this part of the assessment you have to specify and test the data selected for your project with your chosen models. Justify the models’ selection through residual analysis and additional tests (you will have to determine which tests will be applicable for your chosen data type and chosen models).
    Expected length: 2-3 pages

    Project Part C: Final Empirical Project

    The term paper is your opportunity to construct a model and analyze it using econometric methods.
    A good paper will have the following format structure:

    1. Introduction (modified and improved Project Part A)
         a. Why do we care? 
         b. What else is known about this problem? 
         c. What are the limitations of previous studies?
    2. Data 
        a. Data collection
        b. Sources and Descriptive statistics (modified and improved Project Part A)
    3. The model 
        a. Estimation and Testing 
        b. Residual analysis (modified and improved Project Part B)
    4. Results
        a. What are the main findings?
        b. Do you find the empirical results convincing?
        c. Interpret your findings and stress their significance
    5. Conclusion
        a. Summary of main contributions
        b. How do you think the study could be improved?
    6. Reference List

    Each term paper will have an assignment submission cover page. Projects should be up to 15 pages of text (with all references cited in the appropriate text), bibliography, tables and figures, and any appendix material. You must include all relevant computer printouts including one that clearly lists your data in a compact. Your grade will depend on your mastery of the relevant econometric theory and the organization of your paper.

    5. 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:

    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.
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    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.

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