ECMT6002: Semester 1, 2025
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Unit outline_

Unit outlines now display a small icon AI Allowed = AI allowed restricted AI = restricted AI to indicate which assessments allow you to use AI tools such as Microsoft Copilot Chat. Make sure you are aware of how AI can be used, as unauthorised use is a breach of academic integrity.

ECMT6002: Econometric Applications

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

This unit illustrates how econometric methods can be applied to economic data to solve problems that arise in economics and business. Econometric theory provides the techniques needed to quantify the strength and form of relationships between variables. Applied econometrics is concerned with the strategies that need to be employed to use these techniques effectively; to determine which model to specify and whether the data are appropriate. Guidelines for undertaking applied work are discussed. Case studies drawn from economics, marketing, finance, and accounting are also discussed. The unit includes a major econometric modelling project.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
ECMT5001
Corequisites
? 
None
Prohibitions
? 
ECMT6702
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Won-Ki Seo, won-ki.seo@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Supervised exam
? 
Final exam
Covers all unit content but more weighted to material covered in Weeks 7-13
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Supervised test
? 
In-semester test
Paper-based test: short-answer questions covering material from Weeks 1-6
30% Week 07
Due date: 10 Apr 2025 at 17:30
1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment group assignment AI Allowed Applied Project
Applied project / Numerical exercise
20% Week 11
Due date: 15 May 2025 at 23:00
Approximately 4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?
AI allowed = AI allowed ?

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 (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

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

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

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator. 

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions. 

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit. 

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply. 

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site

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.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Linear Regression Model: Simple Linear Regression Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Linear Regression Model: Multiple Linear Regression Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Linear Regression Model: Regression Diagnostics & Robust Inference Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Linear Regression Model: Asymptotic Analysis Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Stochastic Regressors & Endogeneity Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Stochastic Regressors & Endogeneity: Extensions Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 In-semester test Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Time Series Model and Prediction Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Misspecification and related issues Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Panel Data Model & Limited Dependent Model 1 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Panel Data Model & Limited Dependent Model 2 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Panel Data Model & Limited Dependent Model 3 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Review and Recent Developments in Econometrics Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5

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.
  • Compulsory assessment tasks: The following tasks are required to complete this course: (i) groupwork, (ii) in-semester test, and (iii) final exam.

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 required textbook. Some textbooks which may be helpful for this unit of study include:

  • J.M. Wooldridge, Introductory Econometrics: A Modern Approach, 5th or 6th edition, South-Western Cengage Learning.
  • A.C. Cameron & P. Trivedi, Microeconometrics Using Stata, Stata Press, Revised Edition.
  • A.C. Cameron & P. Trivedi, Microeconometrics: Methods and Applications, 1st edition, Cambridge University Press.
  • R. Davidson & J.G. MacKinnon, Econometric Theory and Methods, Oxford University Press.

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 understanding of fundamental econometric methods, such as least squares, instrumental variables, and maximum likelihood
  • LO2. identify and apply appropriate econometric techniques to work with cross-sectional, time-series, and panel data
  • LO3. implement a variety of econometric methods for causal inference
  • LO4. develop the skills needed to evaluate applied economic research
  • LO5. apply appropriate models and estimators to real data sets using specialised econometric software.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Changes to lecture notes and tutorial questions have been made based on student feedback.

Disclaimer

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

This unit of study outline was last modified on 14 Feb 2025.

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