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Course profile

Policy Evaluation Econometrics (ECON7330)

Study period
Sem 2 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 2, 2025 (28/07/2025 - 22/11/2025)
Study level
Postgraduate Coursework
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Economics School

This is an advanced course in econometrics. The focus will be on foundations of econometric theory, including the theory behind parametric, nonparametric and semi-parametric econometrics, and its applicability to policy evaluations.

This course is designed for students interested in the econometric foundations of policy evaluation. The students will learn both econometric techniques and coding skills. The course will start with basic foundations of causal inference and will address some of the recent developments of the most popular applied econometric techniques for policy evaluation.

Course requirements

Assumed background

Elements of statistical and econometric theory.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

ECON7320 or 7321 or 7331 or 7360

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

ECON3300 or 3350 or 3360 or 7350 or 7360

Incompatible

You can't enrol in this course if you've already completed the following:

ECON6310

Course contact

School enquiries

Student Enquiries, School of Economics

All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed to enquiries@economics.uq.edu.au.

Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.

Course staff

Lecturer

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

The timetable is published through the UQ Public Timetable found in the APPs section of myUQ. Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.

Important Dates:

  • Public Holidays: Wed 13 August (Royal Queensland Show Holiday), Mon 6 October (King’s Birthday public holiday).
  • Mid-Semester Break: 29 September – 3 October. Semester 2 classes recommence on Tue 7 October.

Aims and outcomes

The main aim of this course is to provide students with a strong grounding in the theory that underpins many standard econometric methods. The course does not attempt to provide details on the theory behind all possible econometric models, but instead seeks to ensure that students gain sufficient skills to allow them to understand the key issues and hence to be able to read and understand advanced texts and journal articles that they may come across in their future studies.

Learning outcomes

After successfully completing this course you should be able to:

LO1.

Explain the application of asymptotic results to specific econometric models.

LO2.

Examine consistency results for some econometric estimators.

LO3.

Explain the different assumptions behind some advanced econometric results.

LO4.

Explain the differences between different classes of econometric estimators.

LO5.

Develop a mathematical framework for deriving Econometric Results for Policy Evaluations.

Assessment

Assessment summary

Category Assessment task Weight Due date
Tutorial/ Problem Set Assignment 1 34%

5/09/2025 1:00 pm

Tutorial/ Problem Set Assignment 2 33%

10/10/2025 1:00 pm

Tutorial/ Problem Set Assignment 3 33%

10/11/2025 1:00 pm

Assessment details

Assignment 1

Mode
Written
Category
Tutorial/ Problem Set
Weight
34%
Due date

5/09/2025 1:00 pm

Learning outcomes
L01, L03, L04, L05

Task description

The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.

Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

Assignments must be submitted in Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 2

Mode
Written
Category
Tutorial/ Problem Set
Weight
33%
Due date

10/10/2025 1:00 pm

Learning outcomes
L02, L03, L04, L05

Task description

The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.

Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

Assignments must be submitted in Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 3

Mode
Written
Category
Tutorial/ Problem Set
Weight
33%
Due date

10/11/2025 1:00 pm

Learning outcomes
L03, L04, L05

Task description

The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.

Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

Assignments must be submitted in Blackboard.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Cut off Percent Description
1 (Low Fail) 0% - 29%

Absence of evidence of achievement of course learning outcomes.

2 (Fail) 30% - 46%

Minimal evidence of achievement of course learning outcomes.

3 (Marginal Fail) 47% - 49%

Demonstrated evidence of developing achievement of course learning outcomes

4 (Pass) 50% - 64%

Demonstrated evidence of functional achievement of course learning outcomes.

5 (Credit) 65% - 74%

Demonstrated evidence of proficient achievement of course learning outcomes.

6 (Distinction) 75% - 84%

Demonstrated evidence of advanced achievement of course learning outcomes.

7 (High Distinction) 85% - 100%

Demonstrated evidence of exceptional achievement of course learning outcomes.

Additional course grading information

A student’s final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.

Supplementary assessment

Supplementary assessment is available for this course.

Additional assessment information

Plagiarism

The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.

The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM. 

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Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Additional learning resources information

Some additional reading material will be provided to students in some topics.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 4
(28 Jul - 24 Aug)

Lecture

Policy Evaluation Econometric Foundational Topics

Learning outcomes: L01, L02, L03, L04, L05

Week 3

(11 Aug - 17 Aug)

No student involvement (Breaks, information)

Ekka public holiday

No lecture.

Multiple weeks

From Week 5 To Week 9
(25 Aug - 28 Sep)

Lecture

Policy Evaluation Econometric Toolbox Topics

Learning outcomes: L02, L03, L04, L05

Mid Sem break

(29 Sep - 05 Oct)

No student involvement (Breaks, information)

Mid-Semester Break

Multiple weeks

From Week 10 To Week 13
(06 Oct - 02 Nov)

Lecture

Policy Evaluation Econometric Further Topics

Learning outcomes: L02, L03, L05

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.