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Unit of study_

ECOS3904: Applied Macroeconometrics

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

This unit provides an introduction to econometric theory and methods that can be useful for understanding applied (mostly macroeconomic/finance) models and research. It also aims to provide students with the necessary analytical tools for undertaking applied research using time series data and discusses how time series techniques can be applied to other areas of economics such as international trade, energy economics, economics of terrorism. This unit can be both complementary to and substitutive for Applied Microeconometrics, which focuses on empirical methods in applied microeconometrics.

Unit details and rules

Unit code ECOS3904
Academic unit Economics
Credit points 6
A minimum of ((65% in ECOS2902) or (75% in ECOS2002)) and 65% in (ECMT2130 or ECMT2150 or ECMT2950 or ECMT2160)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Ye Lu,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final Exam
Final Exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Problem set 1
Short-answer analytic or computer-based problems
6% Week 03 varies
Outcomes assessed: LO1 LO3
Assignment Problem set 2
Short-answer analytic or computer-based problems
8% Week 06 varies
Outcomes assessed: LO1 LO2 LO3
In-semester test (Open book) Type C in-semester exam Mid-semester test
mid-semester exam
20% Week 07
Due date: 14 Oct 2020 at 10:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Problem set 3
Short-answer analytic or computer-based problems
8% Week 10 varies
Outcomes assessed: LO1 LO2 LO3
Assignment Problem set 4
Short-answer analytic or computer-based problems
8% Week 12 varies
Outcomes assessed: LO1 LO2 LO3 LO4
Type C final exam = Type C final exam ?
Type C in-semester exam = Type C in-semester exam ?

Assessment summary

  • Take-home assignments (30%): students are required to solve the given analytic or computer-based problems individually and submit the solutions online before the due time.
  • Mid-semester test (20%): students are required to complete an online open-book unproctored test (1.5hr writing time) in Week 7.
  • Final exam (50%): students are required to complete an online 2hr open-book unproctored exam during the final exam weeks.

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.


For more information see

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.

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 Introduction to economic time series data and overview of the course Lecture and tutorial (3 hr) LO1 LO3
Week 02 Serial correlation and univariate autoregressions Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 03 Forecasting using time series models Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 04 Models with trends I: deterministic and stochastic trends Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 05 Models with trends II: trend-cycle decomposition Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 06 Structural breaks Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 08 Estimation of dynamic causal effects I Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 09 Estimation of dynamic causal effects II Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 10 Vector autoregressions (VARs) Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 11 Cointegration Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 12 Volatility clustering and ARCH models Lecture and tutorial (3 hr) LO1 LO2 LO3

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

There is no required textbook for this course, but the topics are mostly covered by the following two textbooks:

  • Enders, Walter. 2014. "Applied Econometric Time Series", 4th Edition. Wiley.
  • Stock, James H. and Mark W. Watson. 2019. "Introduction to Econometrics", 4th Edition. Princeton University Press. (Only Chapters 15-17 on regression analysis of economic time series data are relevant)

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. describe and summarise economic time series data using appropriate statistical tools;
  • LO2. master the most widely-used and useful econometric methods for quantifying knowledge about the underlying structure of the macroeconomy, and understand the practical challenges in implementing these methods;
  • LO3. use MATLAB proficiently for a range of macroeconometric analysis;
  • LO4. critically evaluate applied macroeconometric research, and understand some of the problems and solutions that arise in applied work;

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

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

No changes have been made since this unit was last offered


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