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Unit of study_

ECMT3170: Computational Econometrics

This unit provides an introduction to modern computationally intensive algorithms, their implementation and application for carrying out statistical inference on econometric models. Students will learn modern programming techniques such as Monte Carlo simulation and parallel computing to solve econometric problems. The computational methods of inference include Bayesian approach, bootstrapping and other iterative algorithms for estimation of parameters in complex econometric models. Meanwhile, students will be able to acquire at least one statistical programming language.


Academic unit Economics
Unit code ECMT3170
Unit name Computational Econometrics
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

ECMT2160 or ECMT2110
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Peter Exterkate,
Type Description Weight Due Length
Final exam Final exam
2hr final exam covering all material taught in this unit (online exam)
50% Formal exam period 2 hours
Outcomes assessed: LO2 LO3
Assignment Take-home assignment 1
both theory and programming, covering lectures from 25 Feb to 6 Mar (incl)
10% Week 04
Due date: 20 Mar 2020 at 23:59
500 words
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Take-home assignment 2
both theory and programming, covering lectures from 13 Mar to 3 Apr (incl)
15% Week 08
Due date: 24 Apr 2020 at 23:59
1000 words
Outcomes assessed: LO1 LO2 LO3 LO4
Participation Participation (online)
awarded based on active participation in online discussions
10% Week 13 ongoing
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Take-home assignment 3
both theory and programming, covering lectures from 1 May to 22 May (incl)
15% Week 13
Due date: 29 May 2020 at 23:59
1000 words
Outcomes assessed: LO1 LO2 LO3 LO4

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Please note that the assessment information in the Handbook is out of date. The information in this Unit of Study Outline is correct. In particular, there will be no mid-semester exam, but an additional take-home assignment instead.


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 Two lectures this week... Tue 25 Feb: Introduction; Motivation; Matlab. Fri 28 Feb: Simulation techniques Lecture (3 hr)  
Week 02 Every Tuesday from now on: tutorial about last week's lecture. Fri 6 Mar: Simulating critical values; Size and power studies Lecture and tutorial (3 hr)  
Week 03 Fri 13 Mar: Bootstrap Lecture and tutorial (3 hr)  
Week 04 Fri 20 Mar: Subsampling; Randomisation tests Lecture and tutorial (3 hr)  
Week 05 Fri 27 Mar: Simulated maximum likelihood Lecture and tutorial (3 hr)  
Week 06 Fri 3 Apr: Method of simulated moments Lecture and tutorial (3 hr)  
Week 07 Tutorial proceeds as usual. Lecture cancelled due to public holiday Tutorial (1 hr)  
Week 08 Tutorial proceeds as usual. Fri 24 Apr: Nonparametric regression Lecture and tutorial (3 hr)  
Week 09 Fri 1 May: Bayesian analysis - Introduction; Monte Carlo integration Lecture and tutorial (3 hr)  
Week 10 Fri 8 May: Bayesian analysis - Linear regression Lecture and tutorial (3 hr)  
Week 11 Fri 15 May: Bayesian analysis - Markov Chain Monte Carlo methods; Gibbs sampling Lecture and tutorial (3 hr)  
Week 12 Fri 22 May: Bayesian analysis - Metropolis-Hastings sampling Lecture and tutorial (3 hr)  
Week 13 Fri 29 May: Bayesian analysis - Nonparametric regression Lecture and tutorial (3 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. Do note that the participation mark for this unit is not an attendance mark; active participation in 80% of classes is much more valuable than passive attendance in 100% of classes.
  • 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

There is no prescribed textbook for the unit. However, regular reference will be made to:

  • Davidson and MacKinnon (2004), Econometric Theory and Methods.
  • Koop (2003), Bayesian Econometrics.
  • Spanos (1999), Probability Theory and Statistical Inference: Econometric Modelling with Observational Data.

Some text based readings will be made available through the course eReadings system.

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. demonstrate proficiency in the use of programming software
  • LO2. demonstrate increased range of econometric techniques for use in research and applied work
  • LO3. critically evaluate underlying assumption and theories in econometrics
  • LO4. coherently communicate to a professional standard.

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
This unit was completely redesigned in 2018, in order to make more room for actual computational techniques to enable students to use simulation-based inference in later work, be it on the job after graduation or in an Honours or even PhD thesis. This went at the expense of deeper statistical theory, which is now covered in ECMT3160 instead. In short, the unit title of ECMT3170 has become much more reflective of its content. Some inevitable teething problems were brought up in 2018 and this feedback was taken on board when further developing the 2019 iteration of this unit. In particular: - A written mid-semester exam was not the most useful way to test the skills that this unit is intended to confer. It has been replaced by an additional take-home assignment. - The block of lectures about Bayesian analysis at the end of the unit was generally described as very interesting but too rushed. This semester, we will be using the additional lecture that we gain by not having a mid-semester exam to cover this topic at a more manageable pace. - The weekly one-hour tutorial used to be scheduled immediately after the two-hour lecture. I have successfully lobbied to have both meetings scheduled on different weekdays this semester, in order to give you more time to digest the lecture content before applying it to tutorial questions. The only changes made in 2020 are around assignment deadlines, which were found to be too tight in some cases. In particular, having the last assignment due the day before the final exam did not work so well, so it is now due earlier and does not cover any material from the last lecture.

Please note that the assessment information in the Handbook is out of date. The information in this Unit of Study Outline is correct. In particular, there will be no mid-semester exam, but an additional take-home assignment instead.


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.