ECON 7223 - Time Series Econometrics IV
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code ECON 7223 Course Time Series Econometrics IV Coordinating Unit 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 Incompatible ECON 4013 Assessment Typically homework problem sets, Just in time teaching (JiTT), empirical projects, & 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: TBACourse Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
- Use various advanced time series econometric methods, estimation methods and related econometric theories.
- Apply these methods to empirical data or develop new time series econometric theories.
- Use a number of specialist software such as Matlab, Gauss, C++, Stata and Eviews.
- Interpret time series models' estimates and analyze the results.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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.
1-4 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.
1-4 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.
1-4 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.
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Learning Resources
Required Resources
Lecture notes will be posted on MyUni before each lecture.
Textbooks
Computer SoftwareStata Manual Stata Time Series Manual Published by Stata Press J. Hamilton Time Series Analysis Princeton: Princeton 最新糖心Vlog Press, 1994 P. J. Brockwell and R. A. Davis Time Series: Theory and Methods 2nd edition. New York: Springer-Verlag, 1991
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 最新糖心Vlog network can make a request to ITS to install Matlab on their machines.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) 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 最新糖心Vlog Press, 2000 John Y. Campbell, Andrew W. Lo, and A. Craig Mackinlay The Econometrics of Financial Markets Princeton 最新糖心Vlog Press, 1997 Online Learning
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.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, -
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.
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.
NB: The above guide is for private study, that is, study outside of your regular classes.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 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
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Assessment
The 最新糖心Vlog'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 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 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.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:
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 .
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 最新糖心Vlog 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 最新糖心Vlog 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.
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Student Support
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
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- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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