Students
FOBE740 – Quantitative Research Approaches in Business and Economics 2
2017 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Roselyne Joyeux
Contact via roselyne.joyeux@mq.edu.au
E4A440
Thursday 11am to 1pm
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|---|---|
| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
Admission to MRes
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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 focuses on advanced statistical approaches used in Business and Economics and related disciplines. Topics include statistical modelling, time series analysis, ARCH, GARCH model, longitudinal and panel data models, generalized linear models and limited dependent variables. The unit will also consider applications of the above models and techniques to these disciplines.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
General Assessment Information
Details of these assessment tasks will be given in lectures, and will be posted on iLearn.
Tutorial exercises
The weekly exercises require access to a statistical package. Students are expected to attempt the tutorial exercises.
Midterm test
A 60-minute test covering all of the material up to week 7 will be held in lecture time in week 8.
Students must be available during the time of the lecture class to sit the Midterm test. The only exception to this is if a student could not do the test because of documented illness or unavoidable disruption. In these circumstances this student may wish to consult the University’s Disruption to Studies policy- http://www.mq.edu.au/policy/docs/disruption_studies/policy.html If a student satisfies the Disruption to Studies policy they will be required to complete a supplementary assessment task.
Assignment
Students will replicate some of the empirical work presented in a recent journal article. They will need to research relevant literature, access data, apply one or more of the techniques discussed in class to address the problem and write up a report on the same. Submission as per the class timetable and to be further discussed in class.
Late submissions will be accepted after the submission deadline. There will be a deduction of 10% of the total quality-based marks for EACH 24 HOURS period or part thereof that the submission is late (for example, being 25 hours late in submission will occur 20% mark penalty). This penalty does not apply for the cases in which an application for special consideration has been made and has been successful.
This is to be completed as an individual piece of work. A hard copy needs to be submitted as well as a copy uploaded to “Turn-it-in” (via iLearn). The assignment is worth 30% of the course grade.
Class Test
The class test will be of a two-hour duration and will be held in lecture time in week 13.
Students must be available during the time of the lecture class to sit the Class test. The only exception to this is if a student could not do the test because of documented illness or unavoidable disruption. In these circumstances this student may wish to consult the University’s Disruption to Studies policy- http://www.mq.edu.au/policy/docs/disruption_studies/policy.html If a student satisfies the Disruption to Studies policy they will be required to complete a supplementary assessment task.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Participation - class | 10% | No | All session |
| Tutorial questions | 10% | No | All session |
| Midterm | 20% | No | Week 8, Lecture time |
| Assignment | 30% | No | Week 14 |
| Class Test | 30% | No | Week 13, Lecture time |
Participation - class
Due: All session
Weighting: 10%
Participation in lectures and tutorials.
On successful completion you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Tutorial questions
Due: All session
Weighting: 10%
Attempt tutorial questions
On successful completion you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Midterm
Due: Week 8, Lecture time
Weighting: 20%
Class Test
On successful completion you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Assignment
Due: Week 14
Weighting: 30%
In this assignment FOBE740 students will replicate some of the empirical work presented in a recent journal article.
On successful completion you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Class Test
Due: Week 13, Lecture time
Weighting: 30%
Class Test
On successful completion you will be able to:
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Delivery and Resources
Lecture and Tutorial times
Classes for FOBE740 are scheduled as per the class timetable available at http://www.timetables.mq.edu.au/
There will be 3 hours face‐to‐face teaching per week consisting of one two-hour lecture and one hour tutorial. Lectures and tutorials are held in the computer labs.
Technology used and required
If you are enrolled in this unit, you will be listed in the FOBE740 online unit (iLearn). Login at http://ilearn.mq.edu.au/
The site will be used to post any additional lecture slides, handouts, and assignment. The site contains a “forum” to which you may contribute. Please log in to the site on a regular basis.
Required and Recommended Texts and/or Materials
The recommended textbooks for FOBE740 are:
- Hill, C. H., Griffiths, W. E. and Lim, G. C. (2011) Principles of Econometrics (4th ed.) Wiley.
- Wooldridge, J. (2008) Introductory Econometrics: A Modern Approach (4th ed.) Cengage Learning.
- A list of prescribed reading will be developed on the website as the unit progresses.
Teaching and Learning Strategy
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Students are expected to complete all pre-class preparation tasks in advance of that particular class.
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Please make notes summarizing the pre-class readings. These notes do not need to be submitted for assessment; however they will permit discussion of the questions and material in class.
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Students are expected to attend and participate in all classes.
Information
Details of the assessment tasks will be given in lectures and posted on iLearn. You should check iLearn regularly.
About this Unit
This unit is one of core courses of the MRes program for FBE students who require advanced quantitative skills in their research. This unit provides students with an introduction to quantitative research approaches within business, economics and finance. It seeks to develop students understanding of the contexts in which quantitative research can be undertaken and the ability to analyse, conduct, and evaluate quantitative forms of research. It is designed for those who have had little or no quantitative training in their undergraduate degree but who needs quantitative skills for specialisations in the areas of business, demography, economics, finance, and marketing.
Assumed background
A one—semester rigorous introduction to probability and statistics is assumed.
Unit Schedule
Week |
Topic |
Tutorial Topic |
| 1 | Stationarity, Integration and ARIMA Models | Introduction to software |
| 2 | Testing for unit roots and bubbles | Computer exercise |
| 3 | Testing for unit roots and bubbles | Computer exercise |
| 4 | Testing for cointegration | Computer exercise |
| 5 | VAR and VECM | Computer exercise |
| 6 | VAR and VECM | Computer exercise |
| 7 | Testing restrictions in VAR models | Computer exercise |
| Mid | Semester | Break |
| 8 |
SVAR |
Mid Term Test |
| 9 | Impulse response functions | Computer exercise |
| 10 | Volatility models | Computer exercise |
| 11 | Panel data models | Computer exercise |
| 12 | Panel data models | Computer exercise |
| 13 | Class test |
The list of topics above is only provisional and will be changed according to students backgrounds and interests.
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html
Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html
Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Results
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
Supplementary exam information
The dates and details relating to supplementary exams are at the following link:
http://www.businessandeconomics.mq.edu.au/current_students/undergraduate/how_do_i/special_consideration
Policy regarding late submission of assessments
Late assignments will be accepted, but will incur a penalty of 10% of the total available marks for each 24 hour period late or part thereof.
Students who do not submit an assignment will be awarded a mark of zero for that assessment except for cases in which an application for special consideration is made and approved.
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
IT Help
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Graduate Capabilities
PG - Capable of Professional and Personal Judgment and Initiative
Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Learning outcome
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
Assessment tasks
- Participation - class
- Assignment
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Assessment tasks
- Participation - class
- Tutorial questions
- Midterm
- Assignment
- Class Test
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Assessment tasks
- Participation - class
- Tutorial questions
- Midterm
- Assignment
- Class Test
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- Understand a range of generalizations of regression and how to apply them.
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
- Have a broad understanding of Panel Data Models and Dynamic Panel Data Models.
Assessment tasks
- Participation - class
- Tutorial questions
- Assignment
- Class Test
PG - Effective Communication
Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.
This graduate capability is supported by:
Learning outcome
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
Assessment tasks
- Participation - class
- Assignment
PG - Engaged and Responsible, Active and Ethical Citizens
Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues
This graduate capability is supported by:
Learning outcome
- Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
Assessment task
- Participation - class
Research and Practice
- The unit is designed to equip students to embark on their individual higher degree research projects.
- A number of reading, writing and analytical tasks are set. Responses to some of these tasks are discussed in class, whereas others will be submitted for assessment. The tasks will contribute directly to the Research Protocol submission and/or PhD thesis.
- The unit is delivered in accordance with current academic teaching and learning pedagogies.