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Unit of study_

ECMT6003: Applied Business Forecasting

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

This unit aims to provide an introduction to the practice of forecasting in business. Forecasting requires both practical experience in model building and some statistical theory. To blend the theory and practice, many business forecasting examples are discussed. Excel is used to do useful preliminary calculations and plotting. At the end of this unit, students should be able to understand the major techniques of forecasting and be able to intelligently forecast actual business time series using Excel and its extensions. Topics covered include: the aims of forecasting and relation to time series analysis; types of time series; plotting and charting time series; practical examples of forecasting and forecasting issues; growth curve methods; least squares (what you need to know for forecasting); decomposition of time series; elementary exponential smoothing with Excel; serial correlation (and Durbin Watson statistic); applied ARIMA modelling and identifying seasonality and "hidden" periodicities.

Unit details and rules

Unit code ECMT6003
Academic unit Economics
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
ECMT6002 or ECMT6702
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Dakyung Seong, dakyung.seong@sydney.edu.au
Lecturer(s) Ning Xu, n.xu@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam Final exam
Comprehensive Final Exam
40% Formal exam period 24 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 1
Individual assignment
5% Week 04
Due date: 26 Aug 2022 at 13:00
1 week
Outcomes assessed: LO1 LO2 LO3 LO4
In-semester test (Take-home short release) Type D in-semester exam Mid-semester exam
Mid-semester exam
20% Week 07
Due date: 16 Sep 2022 at 13:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment Applied project
Applied project
30% Week 10
Due date: 14 Oct 2022 at 13:00
2500 words
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 2
Individual assignment
5% Week 12
Due date: 28 Oct 2022 at 13:00
1 week
Outcomes assessed: LO1 LO2 LO3 LO4
group assignment = group assignment ?
Type D in-semester exam = Type D in-semester exam ?
Type E final exam = Type E final exam ?

Assessment summary

Assignments 1 and 2 will respectively cover materials from weeks 1 – 4 and 5 – 10. The mid-semester exam will cover materials from weeks 1 – 6. The final exam is comprehensive. The applied project is due in week 10. Each group is expected to conduct an empirical application with real-world data and submit a 10-15 page report. Detailed information for each assessment will be posted in the Canvas site for this unit.

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

For more information see 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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

See detailed information on Canvas

Academic integrity

The Current Student website  provides information on academic integrity 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 integrity breaches seriously.  

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to time series and forecasting Lecture (3 hr)  
Week 02 Stationary ARMA processes Lecture (3 hr)  
Week 03 ARMA: Estimation and Forecasting Lecture (3 hr)  
Week 04 Parameter instability: structural breaks and deterministic trends Lecture (3 hr)  
Week 05 Stochastic trends: unit root tests and ARIMA models Lecture (3 hr)  
Week 06 Predictive regression Lecture (3 hr)  
Week 08 Vector Autoregressive (VAR) Processes: Identification and Estimation Lecture (3 hr)  
Week 09 Vector Autoregressive (VAR) processes: Inference and Forecasting Lecture (3 hr)  
Week 10 Local projection and related topics Lecture (3 hr)  
Week 11 Cointegration and spurious regression Lecture (3 hr)  
Week 12 Conditional Heteroskedasticity and Nonlinearity Lecture (3 hr)  
Week 13 Forecasting with high-dimensional data Lecture (3 hr)  

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

Recommended reading:

  • Diebold, F. X. (2017). Forecasting, Department of Economics, University of Pennsylvania. http://www.ssc.upenn.edu/~fdiebold/Textbooks.html
  • Enders, W. (2014). Applied econometric time series. University of Alabama.
  • Hamilton, J. D. (1994). Time series analysis. Princeton University Press.

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. understand important econometric methods and their applicability to forecasting
  • LO2. utilise regression techniques for forecasting, including ARIMA, GARCH, and VAR modelling
  • LO3. apply a range of models to the forecasting of economic data
  • LO4. evaluate forecast accuracy for different techniques.

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

This section outlines changes made to this unit following staff and student reviews.

Some changes have been made to topics covered.

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.