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Unit of study_

ECMT6702: Econometric Applications A

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.

Details

Academic unit Economics
Unit code ECMT6702
Unit name Econometric Applications A
Session, year
? 
Semester 2, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Prohibitions
? 
ECMT5002 or ECMT6002
Prerequisites
? 
None
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

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
Comprehensive final exam
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
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 LO2 LO3 LO4 LO5
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 ?

Assessments have been and could be further adjusted to the pandemic. The final exam is comprehensive but more weighted to week 7 – 13 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 sydney.edu.au/students/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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Detailed information for late penalties can be found in the Canvas.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

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 Generalized 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 eReserve 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 linear regression models and some nonlinear models
  • LO2. Understand major econometric methods, including least squares, instrumental variable, 2SLS, and maximum likelihood
  • LO3. Interpret the estimates from the application of models
  • 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
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.

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