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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
Credit points Credit points
4
Prerequisites Prerequisites
Admission to MRes
Corequisites Corequisites
Co-badged status Co-badged status
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.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/

Learning Outcomes

  1. Understand a range of generalizations of regression and how to apply them.
  2. Understand how linear models, time series models and various generalizations are applied and how empirical results are communicated in practice.
  3. 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 Due
Participation - class 10% All session
Tutorial questions 10% All session
Midterm 20% Week 8, Lecture time
Assignment 30% Week 14
Class Test 30% Week 13, Lecture time

Participation - class

Due: All session
Weighting: 10%

Participation in lectures and tutorials.


This Assessment Task relates to the following 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.

Tutorial questions

Due: All session
Weighting: 10%

Attempt tutorial questions


This Assessment Task relates to the following 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.

Midterm

Due: Week 8, Lecture time
Weighting: 20%

Class Test


This Assessment Task relates to the following 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.

Assignment

Due: Week 14
Weighting: 30%

In this assignment FOBE740 students will replicate some of the empirical work presented in a recent journal article.

 


This Assessment Task relates to the following 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.

Class Test

Due: Week 13, Lecture time
Weighting: 30%

Class Test


This Assessment Task relates to the following 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.

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:

  1. Hill, C. H., Griffiths, W. E. and Lim, G. C. (2011) Principles of Econometrics (4th 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-class preparation tasks in advance of that particular class.

  •  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. 

  • 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 http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.

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 Enquiry Service

For all student enquiries, visit Student Connect at ask.mq.edu.au

Equity 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.

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 - 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 - 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 - 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.