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Unit outline_

ECMT6002: Econometric Applications

Semester 2, 2021 [Normal day] - Remote

This unit illustrates how econometric methods can be applied to economic data to solve problems that arise in economics and business. Econometric theory provides the techniques needed to quantify the strength and form of relationships between variables. Applied econometrics is concerned with the strategies that need to be employed to use these techniques effectively; to determine which model to specify and whether the data are appropriate. Guidelines for undertaking applied work are discussed. Case studies drawn from economics, marketing, finance, and accounting are also discussed. The unit includes a major econometric modelling project.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
ECMT5001
Corequisites
? 
None
Prohibitions
? 
ECMT6702
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Jian Hong, jian.hong@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam (take home)
Comprehensive final exam
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Online task Problem Set 1
Assignment due in Week 5
10% Week 05
Due date: 22 Mar 2021 at 18:00
1 week
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Online task Problem Set 2
Assignment due in Week 9
10% Week 09
Due date: 03 May 2021 at 18:00
1 week
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Assignment group assignment Applied Project
Applied project
30% Week 13
Due date: 31 May 2021 at 18:00
approximately 7 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?
Type D final exam = Type D final exam ?

Assessment summary

Assessments have been and could be further adjusted to the pandemic. The final exam is comprehensive but more weighted to week 7 – 12 material. Detailed information for each assessment will be posted in the Canvas site for this unit. 

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 Linear Regression Model: Estimation Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Linear Regression Model: Inference Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Generalised Least Squares Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Endogeneity and Instrumental Variable Estimator Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Simultaneous Equations Models and 2SLS Estimator Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Time Series Models and Estimators Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Panel Data Models and Estimators Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Treatment effects, Social and Natural Experiments Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Maximum Likelihood and Numerical Methods Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Discrete Choice Models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Limited Dependent Variable Models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Overview of Machine Learning and its Applications Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Review for the final exam Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5

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

The main reading for this unit of study:

  • Required textbook: Wooldridge, J.M., Introductory Econometrics, A Modern Approach, South-Western Cengage Learning, 5th or 6th Edition
  • Recommended textbook: A.C. Cameron and P.Trivedi, Microeconometrics: Methods and Applications, Cambridge University Press, 1st Edition
  • Recommended textbook: A.C. Cameron and P.Trivedi, Microeconometrics Using Stata, by Cameron & Trivedi, Stata Press, Revised Edition

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

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. list and explain the assumptions underlying the multiple regression model
  • LO2. understand major econometric methods, including least squares, instrumental variable, 2SLS, and maximum likelihood
  • LO3. interpret the estimates from the application of models to data
  • LO4. explain the advantages and disadvantages of various models and estimators
  • LO5. apply appropriate models and estimators to real data sets using specialized econometric software.

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.

Changes to lecture notes and tutorial questions have been made based on student feedback.

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.