ECMT3120: Semester 2, 2023

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Units ECMT3120 Semester 2 2023 [Normal day]

Unit of study_

ECMT3120: Applied Econometrics

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

Overview

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Econometric methods are concerned with the appropriate use of these techniques in practical applications in economics. In this unit general principles for undertaking applied work are discussed and necessary research skills developed. In particular, the links between econometric models and the underlying substantive knowledge or theory for the application are stressed. Topics may include, panel data, partial identification, weak instruments, nonparametric regression and density estimation, and discrete choice models. Research papers involving empirical research are studied.

Unit details and rules

Unit code	ECMT3120
Academic unit	Economics
Credit points	6
Prohibitions ?	ECMT3020
Prerequisites ?	ECMT3110 or ECMT3010 or ((ECMT2150 or ECMT2950) and ECMT2160)
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Yiran Xie, yiran.xie@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Pen-and-paper exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	HW 1 n/a	20%	Multiple weeks	8 x (2-4 practice questions)
Outcomes assessed:
Supervised test ?	Mid-semester test In-class, paper-based test	30%	Week 08 Due date: 20 Sep 2023 at 09:00 Closing date: 20 Sep 2023	1 hour
Outcomes assessed:

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 sydney.edu.au/students/guide-to-grades.

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.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction + Math Preparation	Lecture (3 hr)
Week 02	Identification	Lecture (3 hr)
Week 03	Identification in the Binary Threshold Crossing Model	Lecture (2 hr)
Week 03	Discussion of homework 1	Tutorial (1 hr)
Week 04	Partial Identification	Lecture (2 hr)
Week 04	Discussion of homework 2	Tutorial (1 hr)
Week 05	Discrete Choice Models and Maximum Likelihood Estimation	Lecture (2 hr)
Week 05	Discussion of homework 3	Tutorial (1 hr)
Week 06	Discrete Choice Models and MLE II	Lecture (2 hr)
Week 06	Discussion of homework 4	Tutorial (1 hr)
Week 07	Discrete Choice Models and MLE III	Lecture (2 hr)
Week 07	Discussion of homework 5	Tutorial (1 hr)
Week 08	Mid-semester test	Lecture (2 hr)
Week 09	Pooled Cross Section and Panel Data Methods	Lecture (2 hr)
Week 09	Discussion of Mid-semester test questions	Tutorial (1 hr)
Week 10	Panel Data Methods II	Lecture (2 hr)
Week 10	Discussion of homework 6	Tutorial (1 hr)
Week 11	Identification and Estimation of Game-theoretic Models	Lecture (2 hr)
Week 11	Discussion of homework 7	Tutorial (1 hr)
Week 12	Game-theoretic Models II	Lecture (2 hr)
Week 12	Discussion of homework 8	Tutorial (1 hr)
Week 13	Game-theoretic Models III	Lecture (3 hr)

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

There is no prescribed text. However, frequent reference will be made to:

various academic journal articles (links through the library posted on Canvas);and
these textbooks:

Cameron and Trivedi (2005), Microeconometrics: Methods and Applications.
Cameron and Trivedi (2009), Microeconometrics using Stata.

Learning outcomes

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.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. utilise methods to produce, validate and interpret simple and multiple regression models
LO2. calculate and understand the statistical significance of regression models
LO3. carefully define statistical models
LO4. identify and apply extensions to the regression model which address special features of different data structures, and the testing of inequality restrictions on the regression coefficients
LO5. achieve fluency with methods like properties of least squares estimators, hypothesis testing, instrumental variables, and discrete choice models.

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
Learning outcomes

Responding to student feedback

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

No changes have been made since this unit was last offered.

Additional information

The unit description has been revised from that appearing in the Handbook as follows:

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Econometric methods are concerned with the appropriate use of these techniques in practical applications in economics. This unit provides an introduction to the theory and practice of econometrics, and discusses general principles for undertaking applied work. Topics may include identification, panel data, instrumental variables, nonlinear regression, limited dependent variable models, and game-theoretic models.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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