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Unit of study_

ECMT3120: Applied Econometrics

Semester 2, 2022 [Normal day] - Remote

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Econometric methods are concerned with the appropriate use of these techniques in practical applications in economics. In this unit 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 may include, panel data, partial identification, weak instruments, nonparametric regression and density estimation, and discrete choice models. Research papers involving empirical research are studied.

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 Yiran Xie,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Open-book, take-home
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO2 LO4 LO5 LO3
Assignment HW 1
2.5% Week 02
Due date: 10 Aug 2022 at 12:00

Closing date: 17 Aug 2022
1 week
Outcomes assessed: LO1 LO2 LO3
Assignment HW 2
2.5% Week 03
Due date: 17 Aug 2022 at 12:00

Closing date: 24 Aug 2022
1 week
Outcomes assessed: LO2 LO1 LO3
Assignment HW 3
2.5% Week 04
Due date: 24 Aug 2022 at 12:00

Closing date: 31 Aug 2022
1 week
Outcomes assessed: LO3 LO1 LO2
Assignment HW 4
2.5% Week 05
Due date: 31 Aug 2022 at 12:00

Closing date: 07 Sep 2022
1 week
Outcomes assessed: LO1 LO2 LO3
Assignment HW 5
2.5% Week 06
Due date: 07 Sep 2022 at 12:00

Closing date: 14 Sep 2022
1 week
Outcomes assessed: LO3 LO5 LO4
In-semester test (Open book) Type C in-semester exam Mid-semester test
Open book
30% Week 08
Due date: 21 Sep 2022 at 09:00

Closing date: 21 Sep 2022
2 hours
Outcomes assessed: LO1 LO2 LO4 LO3
Assignment HW 6
2.5% Week 09
Due date: 05 Oct 2022 at 12:00

Closing date: 12 Oct 2022
1 week
Outcomes assessed: LO3 LO5 LO4
Assignment HW 7
2.5% Week 10
Due date: 12 Oct 2022 at 12:00

Closing date: 19 Oct 2022
1 week
Outcomes assessed: LO3 LO5 LO4
Assignment HW 8
2.5% Week 11
Due date: 19 Oct 2022 at 12:00

Closing date: 26 Oct 2022
1 week
Outcomes assessed: LO3 LO4 LO5
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 the Unit and Linear Models (OLS) Lecture (3 hr)  
Week 02 GLS Regressions Lecture (2 hr)  
STATA practice Tutorial (1 hr)  
Week 03 Joint Hypothesis Test and Model Specification Lecture (2 hr)  
Discussion of homework 1 Tutorial (1 hr)  
Week 04 Heteroskedasticity Lecture (2 hr)  
Discussion of homework 2 Tutorial (1 hr)  
Week 05 Instrumental Variables I Lecture (2 hr)  
Discussion of homework 3 Tutorial (1 hr)  
Week 06 Instrumental Variables II Lecture (2 hr)  
Discussion of homework 4 Tutorial (1 hr)  
Week 07 Panel Data I Lecture (2 hr)  
Discussion of homework 5 Tutorial (1 hr)  
Week 08 Panel Data II; Mid-semester exam Lecture (3 hr)  
Week 09 Nonparametrics and Bootstrap Lecture (2 hr)  
Discussion of Mid-semester exam questions Tutorial (1 hr)  
Week 10 Maximum Likelihood and Nonlinear Regression I Lecture (2 hr)  
Discussion of homework 6 Tutorial (1 hr)  
Week 11 Maximum Likelihood and Nonlinear Regression II Lecture (2 hr)  
Discussion of homework 7 Tutorial (1 hr)  
Week 12 Qualitative and Limited Dependent Variable Models I Lecture (2 hr)  
Discussion of homework 8 Tutorial (1 hr)  
Week 13 Qualitative and Limited Dependent Variable Models II 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

Cameron and Trivedi (2005), Microeconometrics: Methods and Applications.

Cameron and Trivedi (2009), Microeconometrics using Stata.

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. utilise methods to produce, validate and interpret simple and multiple regression models
  • LO2. calculate and understand the statistical significance of regression models
  • LO3. carefully define statistical models
  • LO4. 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
  • LO5. achieve fluency with methods like properties of least squares estimators, hypothesis testing, instrumental variables, and discrete choice 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 unit description has been revised from that appearing in the Handbook as follows:

This unit provides an introduction to the theory and practice of econometrics, and discusses general principles for undertaking applied work. Topics may include simple and multiple regression, cross-sectional and panel data, instrumental variables, nonlinear regression, and limited dependent variable models. We will cover theoretical and conceptual foundations, and provide applied examples with programming in STATA.


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