BUSS7904: Semester 2, 2020

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Units BUSS7904 Semester 2 2020 [Normal day]

Unit of study_

BUSS7904: Advanced Quantitative Methods

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit provides students with an introduction to advanced quantitative analysis techniques that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. The unit is divided into four segments. The first segment reviews basic quantitative methods covered in BUSS7902 before considering issues around estimation and forecasting. Focus then switches to approaches for dealing with repeated measures including panel estimation methods and time series analysis, before consideration of ANOVA techniques and analogous non-parametric methods. Consideration is then given to the most widely used multivariate methods including factor analysis, multiple discriminant analysis, cluster analysis and structural equation modelling. The final segment covers categorical and discrete choice data analysis covering both the theory and practice of designing choice experiments and conducting sophisticated logit modelling applications. The unit covers both the theory and application of the various techniques with hands-on lab-based sessions and assignments crucial to the quality of the learning experience.

Unit details and rules

Unit code	BUSS7904
Academic unit	Business School
Credit points	6
Prohibitions ?	ECOF7904
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	BUSS7902
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Laurent Pauwels, laurent.pauwels@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Data analytics problem 1 n/a	25%	Week 04	n/a
Outcomes assessed:
Assignment	Data analytics problem 2 n/a	25%	Week 07	n/a
Outcomes assessed:
Assignment	Data analytics problem 3 n/a	25%	Week 10	n/a
Outcomes assessed:
Assignment	Data analytics problem 4 n/a	25%	Week 13	n/a
Outcomes assessed:

Assessment summary

Data analytics problem 1: A problem set on the material covering approximately weeks 1-3.
Data analytics problem 2: A problem set on the material covering approximately weeks 4-6.
Data analytics problem 3: A problem set on the material covering approximately weeks 7-9.
Data analytics problem 4: A problem set on the material covering approximately weeks 10-13.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
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.

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	Revision of Statistics and Linear Algebra	Block teaching (3 hr)
Week 02	Linear regression model and Least Squares: Motivations, assumptions, algebra of OLS, geometry of OLS and projections matrices	Block teaching (3 hr)
Week 03	Finite and large sample properties of OLS; Inference and hypothesis testing	Block teaching (3 hr)
Week 04	Endogeneity: measurement error, omitted variable bias, and simultaneity. Instrumental Variables: Two-Stage Least Squares, Weak Instruments.	Block teaching (3 hr)
Week 05	Instrumental Variables: Two-Stage Least Squares, Weak Instruments. Generalized Method of Moments: Feasible GMM, HAC	Block teaching (3 hr)
Week 06	Maximum Likelihood Estimation	Block teaching (3 hr)
Week 07	Tests: Wald, LM and LR tests	Block teaching (3 hr)
Week 08	Linear Probability Model. Binary Choice Model (Probit and Logit)	Block teaching (3 hr)
Week 09	Models for Multiple Choices: Multinomial and Conditional Logit Models.	Block teaching (3 hr)
Week 10	Ordered Probit; Non-normality and heteroskedasticity in Choice modelling	Block teaching (3 hr)
Week 11	Tobit Models, Truncated Models and Sample Selection	Block teaching (3 hr)
Week 12	Panel Data Models and Dynamic Panels	Block teaching (3 hr)
Week 12	Time-Series Models	Block teaching (3 hr)
Week 13	Structural Equation Models (time permitting)	Block teaching (3 hr)

Attendance and class requirements

Students should ensure they attend and participate in all classes.

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

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

R. Davidson and J. MacKinnon - Econometric Theory and Methods. (2004, Oxford University Press)
Fumio Hayashi - Econometrics. (2000, Princeton University Press).
A. Colin Cameron, Pravin K. Trivedi - Microeconometrics. Methods and Applications. (2005, Cambridge University Press).
James Hamilton - Time Series Analysis. (1994, Princeton University Press).

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 some fundamental concepts and methods in statistics
LO2. employ advanced statistical models and appropriate statistical methods to analyse the data using statistical packages
LO3. interpret computer output and summarize research findings
LO4. relate statistical knowledge and outcomes to your research project.

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.

No changes have been made since this unit was last offered

Additional information

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

Additional information

Additional information

Disclaimer

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