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Unit outline_

ECMT1020: Introduction to Econometrics

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

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

Academic unit Economics
Credit points 6
Prerequisites
? 
ECMT1010 or ECOF1010 or BUSS1020 or MATH1905 or MATH1005 or MATH1015 or DATA1001 or DATA1901
Corequisites
? 
None
Prohibitions
? 
ECMT1001 or ECMT1002 or ECMT1003 or ECMT1021 or ECMT1022 or ECMT1023
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 Kadir Atalay, kadir.atalay@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Final Exam , problem solving, essay, MCQs (see canvas for details)
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Tutorial quiz Online Quiz 1 (Covers weeks 1, 2 and 3 materials)
Online quiz
5% Week 04
Due date: 17 Sep 2020 at 17:00
n/a (See Canvas announcements)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
In-semester test (Open book) Type C in-semester exam Mid-semester exam
Covers weeks 1 to 6. See Canvas for details.
30% Week 07
Due date: 14 Oct 2020 at 16:00
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Tutorial quiz Online Quiz 2 (covers weeks 4, 5 and 6)
online quiz
5% Week 07
Due date: 12 Oct 2020 at 17:00
n/a (See Canvas announcements)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Tutorial quiz Online Quiz 3 (covers weeks 7, 8 and 9)
online quiz
5% Week 10
Due date: 05 Nov 2020 at 17:00
n/a (See Canvas announcements)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Tutorial quiz Online Quiz 4 (Covers weeks 6 to 12 )
online quiz
5% Week 12
Due date: 20 Nov 2020 at 17:00
n/a (See Canvas announcements)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Type B final exam = Type B final exam ?
Type C in-semester exam = Type C 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 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.

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 1. Types of data; 2. Summarizing univariate data; 3. Introduction to STATA Lecture (2 hr)  
Workshop 1 - Introduction Workshop (2 hr)  
Week 02 1. Review of statistical concepts; 2. Inference on the sample mean Lecture (2 hr)  
Workshop Workshop (2 hr)  
Week 03 1. Further univariate inference; 2. Univariate data transformations; 3. Introduction to Bivariate data Lecture (2 hr)  
Computer lab Computer laboratory (2 hr)  
Week 04 1. Summarizing bivariate data 2. Least squares regression Lecture (2 hr)  
Workshop Workshop (2 hr)  
Week 05 Inference for bivariate regression Lecture (2 hr)  
Computer lab Computer laboratory (2 hr)  
Week 06 Bivariate regression and data transformations Lecture (2 hr)  
Workshop Workshop (2 hr)  
Week 07 Computer lab Computer laboratory (2 hr)  
Week 08 Multivariate least squares regression Lecture (2 hr)  
Workshop Workshop (2 hr)  
Week 09 Inference for multivariate regression 1 Lecture (2 hr)  
Computer lab Computer laboratory (2 hr)  
Week 10 Inference for multivariate regression 2 Lecture (2 hr)  
Workshop Workshop (2 hr)  
Week 11 Multivariate regression and data transformations Lecture (2 hr)  
Computer lab Computer laboratory (2 hr)  
Week 12 Model Mispecification Lecture (2 hr)  
Workshop Workshop (2 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

All readings for this unit can be accessed on the Library eReserve link available in the Canvas site for this unit.

A.C. Cameron (2015), Analysis of Economics Data: An Introduction to Econometrics.

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. 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
  • LO2. critically evaluate underlying theories, concepts, assumptions and arguments in econometrics and other related fields
  • LO3. demonstrate proficiency in carrying out applied work in econometrics and in the use of an econometric software package
  • LO4. develop coherent arguments when recommending solutions
  • LO5. understand the process of econometric model building and testing
  • LO6. display awareness of ethical issues in analysing data and reporting results
  • LO7. confidently and coherently communicate, orally and in writing, to a professional standard
  • LO8. manage, analyse, evaluate and use information efficiently and effectively.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

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

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.