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Unit of study_

ECMT3130: Forecasting for Economics and Business

The need to forecast or predict future values of economic time series arises frequently in many branches of applied economic and commercial work. It is, moreover, a topic which lends itself naturally to econometric and statistical treatment. The specific feature which distinguishes time series from other data is that the order in which the sample is recorded is of relevance. As a result of this, a substantial body of statistical methodology has developed. This unit provides an introduction to methods of time series analysis and forecasting. The material covered is primarily time domain methods designed for a single series and includes the building of linear time series models, the theory and practice of univariate forecasting and the use of regression methods for forecasting. Throughout the unit a balance between theory and practical application is maintained.

Details

Academic unit Economics
Unit code ECMT3130
Unit name Forecasting for Economics and Business
Session, year
? 
Semester 2, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
ECMT3030
Prerequisites
? 
ECMT2010 or ECMT2110 or ECMT2030 or ECMT2130 or ECMT2160
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator David Ubilava, david.ubilava@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
multiple-choice; problem sets; R code assessment
60% Formal exam period 2 hours
Outcomes assessed: LO1
Small continuous assessment Individual assignment
Data analysis and forecasting in R
20% Multiple weeks 1500wd equiv. multiple week submission
Outcomes assessed: LO1
Small test Online Quiz
multiple-choice; coding exercise
20% Multiple weeks 30 minutes
Outcomes assessed: LO1
Type B final exam = Type B final exam ?

Small Test: The small test is a series of online quizzes that will consist of multiple-choice questions, as well as questions related to R coding.

Individual Assignment: The assignment will involve replicating and reproducing a forecasting exercise using R. The concrete details and data will become available around week 4 (give or take a couple of weeks) of the semester. 

Final Exam: The final exam will consist of multiple-choice and quantitative questions, as well as questions related to R coding.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Preliminaries Lecture (2 hr) LO1
Week 02 Preliminaries Lecture and tutorial (3 hr) LO1
Week 03 Deterministic Time Series Models Lecture and tutorial (3 hr) LO1
Week 04 Dynamic Time Series Models Lecture and tutorial (3 hr) LO1
Week 05 Dynamic Time Series Models Lecture and tutorial (3 hr) LO1
Week 06 Dynamic Time Series Models Lecture and tutorial (3 hr) LO1
Week 07 Dynamic Time Series Models Lecture and tutorial (3 hr) LO1
Week 08 Forecast Assessment Lecture and tutorial (3 hr) LO1
Week 09 Forecast Assessment Lecture and tutorial (3 hr) LO1
Week 10 Forecast Assessment Lecture and tutorial (3 hr) LO1
Week 11 Special Topics Lecture and tutorial (3 hr) LO1
Week 12 Special Topics Lecture and tutorial (3 hr) LO1
Week 13 Review Lecture and tutorial (3 hr) LO1

Attendance and class requirements

  • Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
  • Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Suggested readings (published journal articles) for this unit can be accessed via the Library eReserve. These readings will become available progressively throughout the semester.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. demonstrate knowledge in applying econometric techniques to forecast economic variables.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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