Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer
Roselyne Joyeux
E4A440
TBA
Amarjeet Kuar
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
20cp at 2000 level or above including ECON241 or ECON2041 or STAT272 or STAT2372
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit provides an introduction to quantitative economic forecasting. Topics may include: exponential smoothing; ARIMA and vector autoregression. The emphasis of the unit is on the practical aspects of forecasting. Theory is developed only to the point necessary to understand the forecasting procedures introduced in the unit. Students are given regular forecasting exercises throughout the unit. Practical work is carried out using an econometric software package. The objective of the unit is to produce graduates who understand the nature of forecasting problems and can produce sound forecasts for use in business and economic analysis. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Name | Weighting | Hurdle | Due |
---|---|---|---|
Test 1 | 20% | No | Week 7 |
Online final examination | 40% | No | During the end of session formal examination period |
Online tutorials problems | 10% | No | Weeks 2-12 in online tutorials |
Assignment | 30% | No | Monday in week 13 |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 4 hours
Due: Week 7
Weighting: 20%
The 60-minute test will cover all of the material up to week 6.
Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: During the end of session formal examination period
Weighting: 40%
A two-hour exam to be completed in a six hour window will be held during the end of session formal examination period, and will consist of short answer questions that require both calculation and written responses
Assessment Type 1: Problem set
Indicative Time on Task 2: 24 hours
Due: Weeks 2-12 in online tutorials
Weighting: 10%
The tutorial problems/questions will be available on iLearn the week before the tutorial. Solutions will be published during the week following each tutorial. Online participation will be assessed
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 22 hours
Due: Monday in week 13
Weighting: 30%
The assignment will be a written task of less than 1,000 words that will report the results of a forecasting project.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Classes
Required and Recommended Texts and/or Materials
Students are not required to purchase a textbook for ECON3061. A detailed reading list will be on the unit website, and all references are available via the Library eReserve. Students are expected to read this material.
Technology Used and Required
Unit Web Page
The web page for this unit can be found on the iLearn web site.
Week |
Topic |
Tutorials |
Work Due |
Week 1 |
Introduction |
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Week 2 |
Forecast evaluation |
Tutorial 1 |
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Week 3 |
Time series decomposition |
Tutorial 2 |
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Week 4 |
Exponential smoothing |
Tutorial 3 |
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Week 5 |
Exponential smoothing |
Tutorial 4 |
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Week 6 |
Machine Learning |
Tutorial 5 |
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Week 7 |
Machine Learning |
Tutorial 6 |
Test 1 |
Week 8 |
ARIMA |
Tutorial 7 |
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Week 9 |
ARIMA |
Tutorial 8 |
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Week 10 |
ARIMA |
Tutorial 9 |
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Week 11 |
Vector autoregression |
Tutorial 10 |
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Week 12 |
Vector autoregression |
Tutorial 11 |
Assignment |
Week 13 |
Review |
Tutorial 12 |
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