Students

MQBS7040 – Quantitative Research Methods

2020 – Session 2, Special circumstance

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.

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Roselyne Joyeux
roselyne.joyeux@mq.edu.au
Contact via email
E4A440
TBA
Credit points Credit points
10
Prerequisites Prerequisites
Admission to MRes
Corequisites Corequisites
Co-badged status Co-badged status
MQBS8040
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, generalised linear models and limited dependent variables. The unit will also consider applications of the above models and techniques to these disciplines.

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:

  • ULO1: Demonstrate knowledge of a range of generalizations of regression and how to apply them.
  • ULO2: Assess how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
  • ULO3: Estimate and interpret Panel Data Models and Dynamic Panel Data Models.
  • ULO4: Appreciate the relevance and limitations of the econometric methods used.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 2 20% No Week 14
Participation in tutorial 20% No All session
Assignment 1 40% No Week 12
Mid session online test 20% No Week 7

Assignment 2

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 17 hours
Due: Week 14
Weighting: 20%

 

This assignment will consist of questions involving quantitative applied and theoretical tasks.

 
On successful completion you will be able to:
  • Demonstrate knowledge of a range of generalizations of regression and how to apply them.
  • Assess how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
  • Estimate and interpret Panel Data Models and Dynamic Panel Data Models.

Participation in tutorial

Assessment Type 1: Participatory task
Indicative Time on Task 2: 24 hours
Due: All session
Weighting: 20%

 

Students are required to come prepared to tutorials (by completing tutorial questions) and to participate in class discussion.

 
On successful completion you will be able to:
  • Demonstrate knowledge of a range of generalizations of regression and how to apply them.
  • Assess how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
  • Estimate and interpret Panel Data Models and Dynamic Panel Data Models.

Assignment 1

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 30 hours
Due: Week 12
Weighting: 40%

 

Students will replicate some of the empirical work presented in a recent journal article. This is a quantitative analysis task. It should be 2000 words or less not including tables and figures.

 
On successful completion you will be able to:
  • Demonstrate knowledge of a range of generalizations of regression and how to apply them.
  • Assess how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
  • Estimate and interpret Panel Data Models and Dynamic Panel Data Models.
  • Appreciate the relevance and limitations of the econometric methods used.

Mid session online test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 15 hours
Due: Week 7
Weighting: 20%

 

Short answer questions; 60-minute test covering all of the material up to week 6.

 
On successful completion you will be able to:
  • Demonstrate knowledge of a range of generalizations of regression and how to apply them.
  • Assess how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Lecture and Tutorial times

  • Lectures for MQBS7040 are scheduled as per the class timetable available at http://www.timetables.mq.edu.au/
  • There will be 3 hours teaching per week consisting of one two-hour lecture and one hour tutorial.
  • Lectures and tutorials will be held in the computer labs and online.

Technology used and required

  • If you are enrolled in this unit, you will be listed in the MQBS7040 online unit (iLearn). Login at http://ilearn.mq.edu.au/
  • The site will be used to post online lectures, lecture slides, handouts, and assignments.
  • The site contains a “forum” to which you may contribute. Please log in to the site on a regular basis.
  • The main software used in MQBS7040 is Eviews which can be accessed through AppStream

Required and Recommended Texts and/or Materials

The recommended textbooks for MQBS7040 are:

  1. Hill, C. H., Griffiths, W. E. and Lim, G. C. (2018) Principles of Econometrics (5th ed.) Wiley.
  2. 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

  • Students are expected to complete all pre-lecture preparation tasks in advance of that particular lecture.
  • Please make notes summarizing the pre-lectures readings. These notes do not need to be submitted for assessment; however they will permit discussion of the questions and material in class.
  • 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.

Unit Schedule

 

 

 

Week

 

 

Topic

 

 

Tutorial Topic

1

Stationarity, Integration and ARIMA Models

Introduction to software

2

Testing for bubbles

Computer exercise

3

VAR and VECM

Computer exercise

4

SVAR

Computer exercise

5

Impulse response functions

Computer exercise

6

Impulse response functions

Computer exercise

7

Impulse response functions

Mid term test

Mid

Semester

Break

8

Panel data models

Computer exercise

9

Panel data models

Computer exercise

10

Dynamic Panel data models

Computer exercise

11

Panel unit roots

Computer exercise

12

Panel cointegration

Computer exercise

13

 Review

Computer exercise

 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/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/study/getting-started/student-conduct​

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) 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 or if you are a Global MBA student contact globalmba.support@mq.edu.au

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 help you improve your marks and take control of your study.

The Library provides online and face to face support to help you find and use relevant information resources. 

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

If you are a Global MBA student contact globalmba.support@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.