ECMT6003: Semester 2, 2021

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Units ECMT6003 Semester 2 2021 [Normal day]

Unit of study_

ECMT6003: Applied Business Forecasting

Semester 2, 2021 [Normal day] - Remote

Overview

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	Jian Hong, jian.hong@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home extended release)	Final exam Comprehensive Final Exam	50%	Formal exam period	24 hours
Outcomes assessed:
Assignment	Assignment 1 Individual assignment	10%	Week 05 Due date: 09 Sep 2021 at 14:00	1 week
Outcomes assessed:
Assignment	Assignment 2 Individual assignment	10%	Week 09 Due date: 14 Oct 2021 at 14:00	1 week
Outcomes assessed:
Assignment	Applied project Applied project	30%	Week 13 Due date: 11 Nov 2021 at 14:00	1500 words
Outcomes assessed:
= group assignment ? = Type E final exam ?

Assessment summary

Assessments have been and could be further adjusted to the pandemic. Assignment 1 covers materials from weeks 1 – 4 and Assignment 2 from weeks 5 – 8. The final exam is comprehensive. The applied project is due in week 13 and each group is expected to submit a 10-12 page report of econometric analysis of some real world time series. 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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to time series and forecasting	Lecture (3 hr)
Week 02	Moving Average (MA), Autoregressive (AR), and ARMA Models	Lecture (3 hr)
Week 03	ARMA: Model Estimation and Forecasting	Lecture (3 hr)
Week 04	ARMA: Model Selection and Diagnostics	Lecture (3 hr)
Week 05	Non-stationary Time Series, Unit Root Tests, and ARIMA Models	Lecture (3 hr)
Week 06	Forecasting with Seasonality and Cycles	Lecture (3 hr)
Week 07	Forecasting with Trends and Structural Breaks	Lecture (3 hr)
Week 08	Modeling Volatility: ARCH and GARCH Models	Lecture (3 hr)
Week 09	VAR for Multivariate Time Series: Modeling and Estimation	Lecture (3 hr)
Week 10	VAR for Multivariate Time Series: Interpretation and Forecasting	Lecture (3 hr)
Week 11	Cointegration and Spurious Regressions	Lecture (3 hr)
Week 12	New Forecasting Methods from Machine Learning	Lecture (3 hr)
Week 13	Review of topics	Block teaching (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:

Applied Econometric Time Series, 4th, by Walter Enders, Wiley, 2014
Time Series Econometrics, by Klaus Neusser, Springer, 2016
Time Series Econometrics: Learning Through Replication, by John D. Levendis, Springer, 2018
Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, by Terence Mills Academic Press, 2019

The University of Sydney Library provide FREE access to the last three textbooks.

Since we use Stata in class for demonstration, you may find the book below useful

Introduction to time series using Stata, by Sean Becketti, Stata Press 2013

Learning outcomes

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

Some changes have made to topics to be 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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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