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Unit outline_

ECON6101: Special Topic in Economics 1

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit of study consists of a special topic in economics, periodically available through staff research interests and the presence of visiting academic staff. As such, the topic and availability of the unit varies from semester to semester, and students should consult the unit of study outline for details pertinent to their intended semester of enrolment.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
(ECON5001 and ECON5002) or (ECON6701 and ECON6702)
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Moyu Liao, moyu.liao@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Closed-book exam: short-answer questions
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 1
Programming homework: estimate production function on a simulated dataset
15% Week 05
Due date: 24 Mar 2024 at 23:59
Varies
Outcomes assessed: LO1 LO4 LO2
Supervised test
? 
In-semester test
Test on production function estimation
20% Week 07
Due date: 12 Apr 2024 at 13:00

Closing date: 12 Apr 2024
1 hour
Outcomes assessed: LO2
Assignment Assignment 2
Programming homework: estimate demand using contraction mapping
15% Week 11
Due date: 12 May 2024 at 23:59
Varies
Outcomes assessed: LO1 LO4 LO3

Assessment summary

Homework is to be completed in Matlab.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

20% penalty will be applied to homework with late submission.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to structural econometrics, GMM method, and Matlab. Lecture (3 hr) LO1 LO4
Week 02 Firm production model 1: The optimization behavior. Matlab programming tutorial on optimization Lecture (3 hr) LO1 LO2 LO4
Week 03 Firm production model 2: The OP and LP method. Matlab programming tutorial on non-parametric estimation. Lecture (3 hr) LO1 LO2 LO4
Week 04 Firm production model 3: Issues in OP and LP model. GNR and ACF method. Nonparametric identification. Lecture (3 hr) LO1 LO2
Week 05 Firm production model 4: Effects on productivity. Lecture (3 hr) LO1 LO2 LO4
Week 07 Demand Estimation 1: Discrete Choice Model. Tutorial on Matlab: maximal likelihood estimation. Lecture (3 hr) LO1 LO3 LO4
Week 08 Demand Estimation 2: Introduction to the BLP model. Lecture (3 hr) LO1 LO3 LO4
Week 09 Demand Estimation 3: The BLP model implementation. Tutorial on Matlab: Introduction to simulation method. Lecture (3 hr) LO1 LO3 LO4
Week 10 Demand Estimation 4: The contraction mapping method. Lecture (3 hr) LO1 LO3 LO4
Week 11 Econometrics of Game Theory 1: Review of game theory model. Lecture (3 hr) LO1 LO3 LO4
Week 12 Econometrics of Game Theory 2: The maximal likelihood approach to estimation. Lecture (3 hr) LO1 LO3 LO4
Week 13 Econometrics of Game Theory 3: The partial identification approach. Matlab tutorial on partial identification. Lecture (3 hr) LO1 LO3 LO4

Attendance and class requirements

  • Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
  • Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on Canvas. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

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

Readings will be provided in class.

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.

At the completion of this unit, you should be able to:

  • LO1. Become familiar with the idea of structural modelling and reduced form estimation.
  • LO2. Learn the firm behavioural modelling techniques and learn to estimate production function.
  • LO3. Learn the discrete choice model, demand side model, the BLP model; Understand the econometric modelling of game theory.
  • LO4. Understand basic computation methods using MATLAB; Learn to compute GMM estimator.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
LO1         
LO2         
LO3         
LO4         

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

This is a Special Topic unit with a different topic each semester.

The topic for Semester 1, 2024 is:

Structural Econometrics

This unit covers topics in structural econometrics. Students will learn to combine economic theory with statistical tools to analyse various empirical economic settings. Students should be able to analyse simple datasets generated by firm production behaviour, consumer choices, and game theory by the end of the unit. Modern identification techniques and concepts will be introduced. We will also pick up estimation and inference tools that are pervasive in the analysis of structural models.

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.