ECON 4013 - Time Series Econometrics IV (H)

North Terrace Campus - Semester 2 - 2022

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.

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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 or equivalent
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
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

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

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.	1,2
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.	2,5
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.	4,5
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.	3,4,5
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.	5
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.	3,4,5

Required Resources

Lecture notes will be posted on MyUni before each lecture.

Textbooks

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

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 最新糖心Vlog 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 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

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	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
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

N/A

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
Problem set and Computer: see Assessment Detail	Problem solving and computing	Refer to MyUni	20%	1-5
Empirical projects: see Assessment Detail	Formative, reading and computing	Refer to MyUni	40%	1-5
Final Exam	Formative, problem solving and computer exercises	Refer to MyUni	40%	1-5

Assessment Related Requirements

N/A

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.

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

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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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Maintained by:	Course Outlines Administrator

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