ECMT2160: Semester 2, 2020

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Units ECMT2160 Semester 2 2020 [Normal day]

Unit outline_

ECMT2160: Econometric Analysis

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

A proper understanding of econometric methods and estimation techniques is important, as it allows researchers and practitioners to assess economic theorems, predict macroeconomic tendencies, evaluate government policies, etc. After a brief review of probability and statistics, this unit will focus on the econometric analysis of discrete variables, and the econometric analysis of time series data. The lectures and assessments will be application-oriented. Computer software (e.g., Stata, R, Matlab) will be used throughout the unit.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ECMT2150 or ECMT2950 or ECMT2110
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Brendan K Beare, brendan.beare@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Final exam	50%	Formal exam period	2 hours
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Online mid-semester exam	25%	Week 07 Due date: 13 Oct 2020 at 14:00	50 minutes
Outcomes assessed:
Assignment	Take home exam Take home exam	25%	Week 12 Due date: 17 Nov 2020 at 16:00 Closing date: 17 Nov 2020	Two weeks
Outcomes assessed:
= Type B final exam ? = Type B in-semester exam ?

Assessment summary

*Mid-semester exam

*Computational assessment using Matlab

*Final exam

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (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.

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	Matrix algebra	Lecture and tutorial (3 hr)
Week 02	Matrix algebra	Lecture and tutorial (3 hr)
Week 03	Matrix algebra	Lecture and tutorial (3 hr)
Week 04	Matrix algebra	Lecture and tutorial (3 hr)
Week 05	Probability and statistics	Lecture and tutorial (3 hr)
Week 06	Probability and statistics	Lecture and tutorial (3 hr)
Week 07	Mid-semester exam	Project (1 hr)
Week 08	Probability and statistics	Lecture and tutorial (3 hr)
Week 09	Probability and statistics	Lecture and tutorial (3 hr)
Week 10	Linear regression	Lecture and tutorial (3 hr)
Week 11	Linear regression	Lecture and tutorial (3 hr)
Week 12	Linear regression	Lecture and tutorial (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

Please refer to Canvas for list of readings

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. Use matrix algebra to express econometric concepts.
LO2. Understand probabilistic and statistical concepts used in econometrics.

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.

ECMT2160 has been redesigned as of S2 2020. Please refer to the course syllabus on Canvas for up-to-date information.

Additional information

This unit develops foundations for more advanced study in econometrics. It begins with an introduction to matrix algebra, which is an essential part of the language of econometrics. This is followed by a formal introduction to probability and statistics, leading up to the law of large numbers and central limit theorem. Finally, these foundations are applied to the study of the linear regression model, the workhorse of econometric analysis.

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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