ECON 4013 - Advanced Time Series Econometrics IV (H)

North Terrace Campus - Semester 2 - 2016

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. Topics typically include stationarity, unit roots, autoregressive moving average (ARMA), forecasting, maximum likelihood estimation (MLE), spectral analysis, vector autoregression (VAR), Structural vector autoregression (SVAR), and co-integration. The emphasis is on understanding the methods and applying them to real-world data.

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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	N
Prerequisites	ECON 4014
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 exam & final exam.

Course Staff

Course Coordinator: Professor Firmin Doko Tchatoka

Location: Room 4.47, Nexus 10 Tower
Telephone: 8313 1174
Email: firmin.dokotchatoka@adelaide.edu.au

Consultation time: TBA

Course Timetable

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

Course Learning Outcomes

The outcomes of this course are:

1	To learn various advanced time series econometric methods, estimation methods and related econometric theories
2	To apply these methods to empirical data or develop new time series econometric theories
3	Students are expected to be able to write a code in Matlab, Gauss, C++, etc.
4	Students are expected to be able to use Stata, Eviews, and etc, to estimate time series 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)
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
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	2,5
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	4,5
Career and leadership readiness technology savvy professional and, where relevant, fully accredited forward thinking and well informed tested and validated by work based experiences	3,4,5
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	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	3,4,5

Required Resources

Lecture notes will be posted on MyUni before each lecture.

Textbooks

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 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.

Workload

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

N/A

Small Group Discovery Experience

N/A

The University's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
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: see Assessment Detail	Problem solving and computing	Refer to course website on MyUni,	15%	1-5
Midterm Exam: see Assessment Detail	Formative, problem solving and computer exercises	Refer to course website on MyUni,	15%	1-5
Empirical project: see Assessment Detail	Formative, reading and computing	Refer to course website on MyUni,	20%	1-5
Final Exam	Formative, problem solving and computer exercises	Refer to course website on MyUni,	40%	1-5

Assessment Related Requirements

N/A

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

Abstract:

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.

Submission

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)
Grade	Grade reflects following criteria for allocation of grade	Reported 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 .

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 (http://www.adelaide.edu.au/policies/101/) 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
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