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Unit of study_

ECMT3110: Econometric Models and Methods

This unit provides a detailed treatment of the core of modern econometric analysis, with a focus on understanding when and why econometric procedures can be expected to work. Topics covered include linear and nonlinear regression, generalised least squares, instrumental variables, generalised method of moments, and maximum likelihood. Numerical software will be used to illustrate the econometric procedures and concepts discussed.


Academic unit Economics
Unit code ECMT3110
Unit name Econometric Models and Methods
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Remote
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
Final exam (Record+) Type B final exam Final exam
Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
In-semester test (Record+) Type B in-semester exam Mid-semester exam
Mid-semester exam
25% Week 07
Due date: 23 Apr 2021 at 12:00
1.5 hours
Outcomes assessed: LO1 LO2 LO4
Assignment Computational task
Take home exam
25% Week 11
Due date: 21 May 2021 at 12:00
One week
Outcomes assessed: LO4
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

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 Linear regression with matrix notation Lecture and tutorial (2 hr) LO4
Week 02 The geometry of linear regression Lecture and tutorial (3 hr) LO1 LO4
Week 03 The geometry of linear regression Lecture and tutorial (3 hr) LO1 LO4
Week 04 Statistical properties of linear regression Lecture and tutorial (3 hr) LO2 LO4
Week 06 Statistical properties of linear regression Lecture and tutorial (3 hr) LO2 LO4
Week 07 Mid-semester exam Project (2 hr) LO1 LO2 LO4
Week 08 Hypothesis testing in linear regression Lecture and tutorial (3 hr) LO2 LO4
Week 09 Hypothesis testing in linear regression Lecture and tutorial (3 hr) LO2 LO4
Week 10 Confidence intervals for linear regression Lecture and tutorial (3 hr) LO2 LO4
Week 11 Instrumental variables Lecture and tutorial (3 hr) LO3 LO4
Week 12 Instrumental variables Lecture and tutorial (3 hr) LO3 LO4
Week 13 Review Lecture and tutorial (3 hr) LO1 LO2 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 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 link available on Canvas.

  • Required textbook: Econometric Theory and Methods, by Davidson and MacKinnon, Oxford University Press, 2003.
  • Recommended textbook: Introduction to the Theory of Econometrics, by Jan R. Magnus, VU University Press, 2017.

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. Conceptualize linear regression in terms of orthogonal projection in Euclidean space.
  • LO2. Understand the key statistical properties of linear regression.
  • LO3. Understand how instrumental variables may be used to estimate linear relationships in the presence of endogeneity.
  • LO4. Study and implement linear econometric methods using MATLAB.

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
There are many changes to ECMT3110 this year. The opening lectures on matrix algebra were moved to ECMT2160. Lectures will now cover instrumental variables estimation. There will be more discussion of the use of MATLAB. Lectures will be live rather than prerecorded.

The unit description above is outdated and should instead read as follows.

ECMT3110 provides a rigorous treatment of linear regression analysis and related methods, including estimation by instrumental variables. It is aimed at students who have taken an introductory course on linear regression and have had prior exposure to matrix algebra and relevant numerical software. Finite sample and asymptotic properties of linear regression are developed and discussed. The numerical software package MATLAB is used to implement and illustrate tools and concepts.


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