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Unit of study_

ECMT3110: Econometric Models and Methods

This unit extends methods of estimation and testing developed in association with regression analysis to cover econometric models involving special aspects of behaviour and of data. In particular, motivating examples are drawn from dynamic models, panel data and simultaneous equation models. In order to provide the statistical tools to be able to compare alternative methods of estimation and testing, both small sample and asymptotic properties are developed and discussed.


Academic unit Economics
Unit code ECMT3110
Unit name Econometric Models and Methods
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

ECMT2110 or ECMT2010 or ECMT2160
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Brendan Beare,
Type Description Weight Due Length
Assignment Applied project
25% - 9-12 pages
Outcomes assessed: LO1 LO2 LO3 LO4
Final exam Final exam (take home)
Take home final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
In-semester test Online Mid-semester exam
25% Week 07
Due date: 07 Apr 2020 at 12:00
1.5 hours
Outcomes assessed: LO1 LO3 LO2

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

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

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Introduction, review of basic matrix algebra and calculus Lecture (2 hr)  
Week 02 Review of probability, OLS - small sample properties Lecture (2 hr)  
Tutorial 1 Tutorial (1 hr)  
Week 03 Review of statistics, OLS - large sample properties Lecture (2 hr)  
Tutorial 2 Tutorial (1 hr)  
Week 04 Heteroskedasticity, Generalized Least Squares (GLS) and feasible GLS Lecture (2 hr)  
Tutorial 3 Tutorial (1 hr)  
Week 05 Endogeneity, instrumental variable method, 2SLS Lecture (2 hr)  
Tutorial 4 Tutorial (1 hr)  
Week 06 Methods of moments Lecture (2 hr)  
Tutorial 5 Tutorial (1 hr)  
Week 07 Midsemester exam Lecture and tutorial (1.5 hr)  
Week 08 Nonlinear Regession and Penalized Regression Lecture (2 hr)  
Tutorial 7 Tutorial (1 hr)  
Week 09 Tutorial 6 Tutorial (1 hr)  
Generalized Method of Moments (GMM) Lecture (2 hr)  
Week 10 Maximum likelihood - estimation and inference, numerical methods Lecture (2 hr)  
Tutorial 8 Tutorial (1 hr)  
Week 11 Maximum likelihood - models and applications Lecture (2 hr)  
Tutorial 9 Tutorial (1 hr)  
Week 12 Hypothesis testing (t, F, LR, LM, and Wald tests) Lecture (2 hr)  
Tutorial 10 Tutorial (1 hr)  
Week 13 Treatment effects, social and natural experiments Lecture (2 hr)  
Tutorial 11 Tutorial (1 hr)  

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 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.

Required readings

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

  • Required textbook: Econometric Theory and Methods, by Davidson and MacKinnon, Oxford University Press, 2003.
  • Required textbook: A Primer in Econometric Theory, by John Stachurski, MIT Press, 2016.

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. understand modern econometric methods and models, including linear and nonlinear models
  • LO2. understand important estimation methods, including ordinary and nonlinear least squares, 2SLS, instrumental variable method, method of moments, generalized method of moments, and maximum likelihood
  • LO3. evaluate existing empirical studies using the econometric methods and models covered
  • LO4. apply the econometric methods and models to data sets in economics and other fields.

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
Lecture notes, tutorial questions, and applied project have been updated according to the student feedback.


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