ECMT1020: Semester 1, 2022

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Units ECMT1020 Semester 1 2022 [Normal day]

Unit of study_

ECMT1020: Introduction to Econometrics

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit is intended to be an introduction to the classical linear regression model (CLRM), the underlying assumptions, and the problem of estimation. Further, we consider hypothesis testing, and interval estimation, and regressions with dummy variables and limited dependent variable models. Finally, we consider different functional forms of the regression model and the problem of heteroskedasticity. Throughout we will try to emphasise the essential interplay between econometric theory and economic applications.

Unit details and rules

Unit code	ECMT1020
Academic unit	Economics
Credit points	6
Prohibitions ?	ECMT1001 or ECMT1002 or ECMT1003 or ECMT1021 or ECMT1022 or ECMT1023
Prerequisites ?	ECMT1010 or ECOF1010 or BUSS1020 or MATH1905 or MATH1005 or MATH1015 or DATA1001 or DATA1901
Corequisites ?	None
Assumed knowledge ?	Students enrolled in this unit have an assumed knowledge equal to or exceeding 70 or higher in HSC Mathematics (or equivalent), or 35 or higher in HSC Mathematics Extension 1 (or equivalent), or 35 or higher in HSC Mathematics Extension 2 (or equivalent)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Ye Lu, ye.lu1@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Multiple-choice, fill-in-the-blank, and short-answer questions	40%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Quiz 1 Multiple choice and fill-in-the-blank questions	3.75%	Week 03 Due date: 08 Mar 2022 at 18:00	1 hour
Outcomes assessed:
Tutorial quiz	Quiz 2 Multiple choice and fill-in-the-blank questions	3.75%	Week 05 Due date: 22 Mar 2022 at 18:00	1 hour
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Multiple-choice and fill-in-the-blank questions	30%	Week 07 Due date: 05 Apr 2022 at 09:00	1.5 hours
Outcomes assessed:
Tutorial quiz	Quiz 3 Multiple choice and fill-in-the-blank questions	3.75%	Week 10 Due date: 03 May 2022 at 18:00	1 hour
Outcomes assessed:
Tutorial quiz	Quiz 4 Multiple choice and fill-in-the-blank questions	3.75%	Week 12 Due date: 17 May 2022 at 18:00	1 hour
Outcomes assessed:
Assignment	Assignment Take-home assignment with 10 short-answer questions	15%	Week 12 Due date: 20 May 2022 at 23:59	one month
Outcomes assessed:
= Type B final exam ? = Type B in-semester exam ?

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 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 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	Review: random variables, sampling, estimation, and inference I	Lecture (2 hr)
Week 02	Review: random variables, sampling, estimation, and inference II	Lecture (2 hr)
Week 02	Workshop 1	Workshop (2 hr)
Week 03	Simple regression analysis	Lecture (2 hr)
Week 03	Workshop 2	Workshop (2 hr)
Week 04	Properties of the regression coefficients and hypothesis testing	Lecture (2 hr)
Week 04	Workshop 3	Workshop (2 hr)
Week 05	Multiple regression analysis	Lecture (2 hr)
Week 05	Workshop 4	Workshop (2 hr)
Week 06	Nonlinear models and transformation of variables	Lecture (2 hr)
Week 06	Workshop 5	Workshop (2 hr)
Week 08	Dummy variables	Lecture (2 hr)
Week 08	Workshop 6	Workshop (2 hr)
Week 09	Specification of regression variables	Lecture (2 hr)
Week 09	Workshop 7	Workshop (2 hr)
Week 10	Heteroskedasticity	Lecture (2 hr)
Week 10	Workshop 8	Workshop (2 hr)
Week 11	Stochastic regressors and measurement errors	Lecture (2 hr)
Week 11	Workshop 9	Workshop (2 hr)
Week 12	Models using time series data I	Lecture (2 hr)
Week 12	Workshop 10	Workshop (2 hr)
Week 13	Models using time series data II	Lecture (2 hr)
Week 13	Workshop 11	Workshop (2 hr)

Attendance and class requirements

Attendance: The lectures are delivered remotely, and the workshops have both face-to-face and remote streams. The students should keep up with the weekly learning schedule by attending both the lectures and workshops.
Lecture recording: The lecture recordings will be made available on Canvas. However, you should not rely on lecture recording to substitute your live learning experience whenever possible.
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

The required textbook for this unit is:

Introduction to Econometrics, 5th edition, by Christopher Dougherty, Oxford University Press (April 2016), ISBN: 9780199676828.

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. understand the process of econometric model building and testing
LO2. critically evaluate underlying theories, concepts, assumptions and arguments in econometrics and other related fields
LO3. develop coherent arguments when recommending solutions
LO4. manage, analyse, evaluate and use information efficiently and effectively
LO5. demonstrate an increased range of econometric techniques for use in applied work and be able to implement this knowledge for modelling data in different situations
LO6. demonstrate proficiency in carrying out applied work in econometrics and in the use of an econometric software package
LO7. confidently and coherently communicate, orally and in writing, to a professional standard

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.

The unit coordinator appreciates all the feedback from past students.

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

Disclaimer

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