Skip to main content
Unit outline_

ECMT2150: Intermediate Econometrics

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit provides an introduction to the econometrics of cross-section and panel data. We start with a discussion of the assumptions underlying the simple and multiple linear regression model. We then build an understanding of the econometric methods available when these assumptions do not hold. More specifically, we cover heteroscedasticity and GLS, omitted variable bias, measurement error and instrumental variables. We finish with an introduction to using pooled cross sections and panel data for policy analysis and to estimate treatment effects. Throughout the unit, emphasis is placed on economic applications of the models and practical computer applications are incorporated.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
(ECMT1010 or BUSS1020 or ((MATH1005 or MATH1905) and MATH1115) or MATH1015 or DATA1001 or DATA1901 or ENVX1002) and (ECMT1020 or QBUS1040 or MATH1002 or MATH1902 or MATH1014 or DATA1002 or DATA1902)
Corequisites
? 
None
Prohibitions
? 
ECMT2110 or ECMT2950
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Luke Hartigan, luke.hartigan@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Paper-based exam: MCQ, true/false, short-answer, & problem-style questions
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO6 LO7 LO8 LO9
Small continuous assessment Quizzes
Multiple-choice questions
10% Multiple weeks Short quiz of 6-10 questions
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Creative assessment / demonstration group assignment Econ&Me Team proposal
Team agreement and preliminary proposal for Econ&Me creative project
1% Week 06
Due date: 08 Sep 2024 at 23:59
200 words
Outcomes assessed: LO4 LO6 LO7 LO8 LO9
Supervised test
? 
In-semester test
Paper-based test: multiple-choice, true/false, and short-answer questions
20% Week 07
Due date: 11 Sep 2024 at 10:00

Closing date: 11 Sep 2024
1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO6 LO7 LO8 LO9
Creative assessment / demonstration group assignment Econ&Me Creative Project
Creative exposition/explanation of a key econometrics concept
9% Week 10
Due date: 13 Oct 2024 at 23:59
Multimedia presentation: 3-5 min
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Skills-based evaluation Problem set
Use of econometric software to answer questions related to a real dataset
10% Week 12
Due date: 27 Oct 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
group assignment = group assignment ?

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The Problem Set and the Econ&Me Team Proposal and Creative Project all have a maximum extension of 10 calendar days in order to accommodate the timely return of marked work. Work not submitted on time will be subject to a late penalty in accordance with University policy. Students experiencing any misadventure must apply for Special Consideration. The Quizzes must be completed within the week they are set. No extensions can be granted and no late submissions will be accepted to allow for timely feedback.

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 Introduction to Econometrics and Some concepts in Probability and Statistics Lecture and tutorial (3 hr) LO1 LO4 LO7 LO9
Week 02 Multiple Linear Regression – Assumptions and Properties Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Inference in Linear Regression Models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 04 Asymptotic Properties of the Linear Regression Model and Using Qualitative Data Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 05 Specification Issues in Linear Regression Models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 06 Further Specification Issues in Linear Regression Models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 08 Heteroskedasticity and Introduction to GLS Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 09 Endogeneity and Instrumental Variables Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 10 Instrumental Variables – Further Issues Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 11 Panel Data Methods Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 12 Panel Data Methods Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 13 Panel Data Methods and Review Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

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

Required text: Wooldridge, J. M., Introductory Econometrics: A Modern Approach, South-Western Cengage Learning.

Please note the latest edition is the 7th edition. You can use that or the 6th edition.

All other readings for this unit can be accessed through the Library’s reading list system, available on Canvas.

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. Develop a foundation of probability theory and statistical knowledge to build an econometric skill set
  • LO2. Be familiar with the linear regression model
  • LO3. Be aware of the guidelines for using econometric techniques effectively
  • LO4. Develop the skills needed to evaluate applied economic research
  • LO5. Develop proficiency in the use of econometric software for econometric modelling
  • LO6. Demonstrate problem-solving skills in research and inquiry
  • LO7. Evaluate ideas, views, and evidence to demonstrate proficient information literacy
  • LO8. Demonstrate critical thinking in research and inquiry
  • LO9. Demonstrate social and professional understanding as well as ethical awareness in analysing data and reporting results.

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.

Topics and assessments have been adjusted in light of student suggestions.

Computer Software: STATA

Throughout this unit you will be required to use a computer and specialised econometric software. The statistics and data analysis program STATA will be taught as part of this unit and will be regularly demonstrated during tutorials and lectures.

Accessing STATA

This software is available to students enrolled in this unit in the teaching labs, the learning hubs around campus and on your own devices through a School of Economics licence.

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.