Outline

Overview

In this unit students will learn a combination of theoretical economic tools and related statistical techniques to be able to build and estimate structural econometric models. Students will be exposed to and be required to apply standard inference and estimation tools used in structural econometric estimations. Students will apply these techniques to datasets to analyse canonical structural economic questions related to topics that may include firm production behaviour, consumer choices, and game theoretic predictions.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ECMT6002 or ECMT6702
Corequisites ?	None
Prohibitions ?	ECON4920
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Moyu Liao, moyu.liao@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Closed-book, paper-based exam: short-answer questions	50%	Formal exam period	2 hours
Outcomes assessed:
Supervised test ?	In-semester test Closed-book, paper-based test: production function estimation	20%	Week 07 Due date: 07 Apr 2025 at 18:30	1 hour
Outcomes assessed:
Assignment	Assignment 1 Programming homework: estimate production function on a simulated dataset	15%	Week 08 Due date: 20 Apr 2025 at 23:59	2 weeks
Outcomes assessed:
Assignment	Assignment 2 Programming homework: estimate demand using contraction mapping; Bootstrap	15%	Week 12 Due date: 25 May 2025 at 23:59	2 weeks
Outcomes assessed:
= AI allowed ?

Assessment summary

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.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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:

Late penalty: for each day of late submission of homework, a 5% penalty will be applied up to 20% penalty. Any less than 24 hours late will be counted as a day. Late submission of more than one week will not be accepted without special consideration.

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.

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.

Support for students

The Support for Students Policy 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. 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to structural econometrics, GMM method, and Matlab.	Lecture and tutorial (3 hr)
Week 02	Firm production model 1: The optimization behavior. Matlab programming tutorial on optimization	Lecture and tutorial (3 hr)
Week 03	Firm production model 2: The OP and LP method. Matlab programming tutorial on non-parametric estimation.	Lecture and tutorial (3 hr)
Week 04	Firm production model 3: Issues in OP and LP model. GNR and ACF method. Nonparametric identification.	Lecture and tutorial (3 hr)
Week 05	Firm production model 4: Effects on productivity.	Lecture and tutorial (3 hr)
Week 06	Demand Estimation 1: Discrete Choice Model. Tutorial on Matlab: maximal likelihood estimation.	Lecture and tutorial (3 hr)
Week 07	In-semester test	Lecture (1 hr)
Week 08	Demand Estimation 2: Introduction to the BLP model.	Lecture and tutorial (3 hr)
Week 09	Demand Estimation 3: The BLP model implementation. Tutorial on Matlab: Introduction to simulation method.	Lecture and tutorial (3 hr)
Week 10	Demand Estimation 4: The contraction mapping method.	Lecture and tutorial (3 hr)
Week 11	Econometrics of Game Theory 1: Review of game theory model.	Lecture and tutorial (3 hr)
Week 12	Econometrics of Game Theory 2: The maximal likelihood approach to estimation.	Lecture and tutorial (3 hr)
Week 13	Econometrics of Game Theory 3: The partial identification approach. Final Review.	Lecture and tutorial (3 hr)

Attendance and class requirements

Attendance: students 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 which 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 the LMS. 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.

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. Become familiar with the idea of structural modeling and reduced form estimation.
LO2. Learn the firm behavioural modellng 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
Learning outcomes

Responding to student feedback

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

This is the first time this unit has been offered.

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

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ECMT6009: Structural Econometrics

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links