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Unit of study_

ECMT3120: Applied Econometrics

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Applied Econometrics is concerned with the appropriate use of these techniques in practical applications in economics and business. General principles for undertaking applied work are discussed and necessary research skills developed. In particular, the links between econometric models and the underlying substantive knowledge or theory for the application are stressed. Topics will include error correction models, unit roots and cointegration and models for cross section data, including limited dependent variables. Research papers involving empirical research are studied and the unit features all students participating in a group project involving econometric modelling.

Unit details and rules

Unit code ECMT3120
Academic unit Economics
Credit points 6
ECMT3110 or ECMT3010 or (ECMT2150 and ECMT2160)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Rami Tabri,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Open book take-home final exam
50% Formal exam period 3 hours (including reading time)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment HW 1
2.5% Week 03
Due date: 08 Sep 2020 at 15:00

Closing date: 08 Sep 2020
1 week
Outcomes assessed: LO3 LO5
Assignment HW 2
2.5% Week 04
Due date: 15 Sep 2020 at 15:00

Closing date: 15 Sep 2020
1 week
Outcomes assessed: LO1 LO5
Assignment HW 3
2.5% Week 05
Due date: 22 Sep 2020 at 15:00

Closing date: 15 Sep 2020
1 week
Outcomes assessed: LO1 LO2 LO3
Assignment HW 4
2.5% Week 06
Due date: 29 Sep 2020 at 15:00

Closing date: 29 Sep 2020
1 week
Outcomes assessed: LO3
In-semester test (Open book) Type C in-semester exam Mid-semester test
Open book take-home Mid-semester test
30% Week 07
Due date: 13 Oct 2020 at 15:00

Closing date: 03 Aug 2020
3 hours (including reading time)
Outcomes assessed: LO1 LO2 LO3 LO5
Assignment HW 5
2.5% Week 07
Due date: 06 Oct 2020 at 15:00

Closing date: 06 Oct 2020
1 week
Outcomes assessed: LO3
Assignment HW 6
2.5% Week 09
Due date: 27 Oct 2020 at 15:00

Closing date: 27 Oct 2020
1 week
Outcomes assessed: LO3
Assignment HW 7
2.5% Week 10
Due date: 03 Nov 2020 at 15:00

Closing date: 03 Nov 2020
1 week
Outcomes assessed: LO3
Assignment HW 8
2.5% Week 11
Due date: 10 Nov 2020 at 15:00

Closing date: 10 Nov 2020
1 week
Outcomes assessed: LO4
Type C in-semester exam = Type C in-semester exam ?
Type D final exam = Type D final exam ?

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 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

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Discussion of course and introduction to statistical models Lecture (2 hr)  
Week 02 Statistical models and Weak Exogeniety Lecture (2 hr)  
Short intro to Matlab Tutorial (1 hr)  
Week 03 Identification in statistical models Lecture (2 hr)  
Discussion of homework 1 Tutorial (1 hr)  
Week 04 Weak identification and Inference Lecture (2 hr)  
Discussion of homework 2 Tutorial (1 hr)  
Week 05 Panel data 1 Lecture (2 hr)  
Discussion of homework 3 Tutorial (1 hr)  
Week 06 Panel data 2 Lecture (2 hr)  
Discussion of homework 4 Tutorial (1 hr)  
Week 07 Midterm Lecture (3 hr)  
Discussion of homework 5 Tutorial (1 hr)  
Week 08 Discrete choice 1 Lecture (2 hr)  
Discussion of Mid-semester exam Tutorial (1 hr)  
Week 09 Discrete choice 2 Lecture (2 hr)  
Discussion of homework 6 Tutorial (1 hr)  
Week 10 Discrete choice 3 Lecture (2 hr)  
Discussion of homework 7 Tutorial (1 hr)  
Week 11 Kernel density estimation and inference Lecture (2 hr)  
Discussion of homework 8 Tutorial (1 hr)  
Week 12 Review Session Lecture (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.
  • 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.

  • Textbook: Davidson and MaCkinnon (2004), Econometric Theory and Methods.
  • Textbook: Econometrics by Badi Baltagi.

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 the pitfalls of weakly identified statistical models
  • LO2. conduct hypothesis testing using various weakly identified simultaneous equations models
  • LO3. identify and apply extensions to the regression model which address special features of different data structures, and the testing of inequality restrictions on the regression coefficients
  • LO4. describe and explain kernel density and regression estimation and testing techniques and appreciate their advantages and disadvantages, in comparison to parametric approaches
  • LO5. carefully define statistical models.

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

No changes have been made since this unit was last offered.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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