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Unit outline_

ECMT1020: Introduction to Econometrics

Intensive January - February, 2025 [Block mode] - Camperdown/Darlington, Sydney

This unit introduces econometrics: the application of economic theory and statistical methods to quantify economic phenomena and relationships using observational economic data. The unit starts with a review of probability and statistics, followed by an in-depth analysis of classical linear regression. Advanced topics including nonlinear models, dummy variables, model specification, heteroskedasticity, measurement errors, and time series are discussed in the second half of the unit. The aim is to develop a deep understanding of the statistical properties of modern econometrics techniques, and to provide the practical skills, such as using statistical software, to fit models to data.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
ECMT1010 or BUSS1020 or DATA1001 or DATA1901 or (MATH1005 and MATH1115) or (MATH1905 and MATH1115) or ENVX1002
Corequisites
? 
None
Prohibitions
? 
ECMT1001 or ECMT1002 or ECMT1003 or ECMT1021 or ECMT1022 or ECMT1023
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Felipe Queiroz Pelaio, felipe.queirozpelaio@sydney.edu.au
The census date for this unit availability is 31 January 2025
Type Description Weight Due Length
Supervised exam
? 
Final exam
Multiple-choice, fill-in-the-blank, and short-answer questions
50% March exam week 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small test Quiz 1
Multiple-choice and fill-in-the-blank questions #earlyfeedbacktask
3% Week 01
Due date: 24 Jan 2025 at 17:00
1 hour
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Small test Quiz 2
Multiple-choice and fill-in-the-blank questions
3% Week 02
Due date: 31 Jan 2025 at 17:00
1 hour
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Supervised test
? 
In-semester test
Multiple-choice, fill-in-the-blank, and short-answer questions
25% Week 03
Due date: 03 Feb 2025 at 14:00
1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small test Quiz 3
Multiple-choice and fill-in-the-blank questions
4% Week 03
Due date: 07 Feb 2025 at 17:00
1 hour
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Assignment Assignment
Short-answer empirical questions
10% Week 04
Due date: 14 Feb 2025 at 23:59
1 week
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Tutorial quiz Workshop Quizzes
Practice questions to be solved in groups
5% Weekly Varies
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 Lecture 1: Introduction and Random Variables I + Lecture 2: Introduction and Random Variables II + Lecture 3: Sampling, Estimation and Statistical Inference Block teaching (6 hr) LO1 LO2
Workshops 1 and 2. Block teaching (4 hr) LO2 LO3 LO5 LO6 LO7
Week 02 Lecture 4: Simple Regression Analysis I + Lecture 5: Simple Regression Analysis II + Lecture 6: Multiple Regression Analysis Block teaching (6 hr) LO1 LO2 LO3 LO4 LO5
Workshops 3, 4 and 5. Block teaching (6 hr) LO2 LO3 LO5 LO6 LO7
Week 03 Lecture 7: Nonlinear Models + Lecture 8: Dummy Variables + Lecture 9: Model Specification Block teaching (6 hr) LO1 LO2 LO3 LO4 LO5
Workshops 6, 7 and 8. Block teaching (6 hr) LO2 LO3 LO5 LO6 LO7
Week 04 Lecture 10: Heteroskedasticity + Lecture 11: Stochastic Regressors + Lecture 12: Review Block teaching (6 hr) LO1 LO2 LO3 LO4 LO5
Workshops 9, 10, 11 and 12. Block teaching (8 hr) LO2 LO3 LO5 LO6 LO7

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. Failure to take in-semester or final exams will result in an absence failure regardless of other outcomes.
  • 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

Required textbook: Dougherty, C. (2016). Introduction to econometrics (5th ed.). 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. understand the process of econometric model building and testing
  • LO2. critically evaluate underlying theories, concepts, assumptions and arguments in econometrics and other related fields
  • LO3. develop coherent arguments when recommending solutions
  • LO4. manage, analyse, evaluate and use information efficiently and effectively
  • LO5. demonstrate an increased range of econometric techniques for use in applied work and be able to implement this knowledge for modelling data in different situations
  • LO6. demonstrate proficiency in carrying out applied work in econometrics and in the use of an econometric software package
  • LO7. confidently and coherently communicate, orally and in writing, 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Change in Assessment Criteria. In addition, attendance rule has been changed: Failure to take in-semester or final exams will result in an absent failure regardless of other outcomes.

Disclaimer

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.