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Unit of study_

ECMT2150: Intermediate Econometrics

Semester 1, 2023 [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

Unit code ECMT2150
Academic unit Economics
Credit points 6
ECMT2110 or ECMT2950
((ECMT1010 or MATH1905 or MATH1005 or MATH1015 or DATA1001 or DATA1901 or ENVX1002) and (ECMT1020 or MATH1002 or MATH1902 or MATH1014 or DATA1002 or DATA1902)) or BUSS1020
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Rebecca Edwards,
Tutor(s) Felipe Queiroz Pelaio,
Type Description Weight Due Length
Monitored exam
Final exam
Online exam: MCQ, true/false, short-answer, and 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
Monitored test
In-semester test
Multiple-choice, true/false, short-answer, and problem-style questions
25% Week 08
Due date: 18 Apr 2023 at 14:00
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO6 LO7 LO8 LO9
Assignment Assignment
Use of econometric software to solve problems and write a report
15% Week 12
Due date: 21 May 2023 at 23:59
Problem Set plus 300-400 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

Assessment summary

Detailed information for each assessment can be found in the Canvas site for this unit.

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


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.

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 Assignment has a maximum extension of 10 calendar days in order to accommodate the timely return of marked assignments. 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.

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 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
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 - I Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 06 Specification Issues in Linear Regression Models - II Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 07 Specification Issues - III: Heteroskedasticity Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 09 Linear probability model and Stata intensive Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 10 Endogeneity and Instrumental Variables Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Week 11 Instrumental Variables – Further Issues. Introduction to Policy Analysis with Pooled Cross Sections and Panel Data 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 continued 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.

Additional required readings for this unit can be accessed on the Library ‘Reading List’ link available in the Canvas site for this unit.

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

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

Topics and assessments have been adjusted slightly in line with 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 via the UniConnect Cloud.
  • You can, but it is not necessary to, purchase your own STATA licence. See A 6-month licence to the latest version of STATA/BE is $81. This version of Stata will be sufficient for any tutorial or assignment in this unit.


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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