ECMT6007: Semester 1, 2022

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Units ECMT6007 Semester 1 2022 [Normal day]

Unit outline_

ECMT6007: Analysis of Panel Data

Semester 1, 2022 [Normal day] - Remote

Overview

Recently, empirical research in economics, finance, marketing and accounting has been enriched by the increasing availability of new sources of data, known as panel data. A 'panel' refers to the pooling of observations on a cross section of households, countries, firms etc. over several time periods. Panel data sets possess several major advantages over conventional cross-sectional or time series data sets. This unit aims to offer a comprehensive treatment of the analysis of panel data, which will allow students to deal in a pragmatic way with fundamental issues, such as controlling for individual heterogeneity, reducing collinearity among regressors, addressing statistical hypotheses and identifying effects that are simply not detectable in pure cross-section or time series data.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ECMT6002 or ECMT6702
Corequisites ?	None
Prohibitions ?	None
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)	Final exam (take home) Take home exam	50%	Formal exam period	3 hours
Outcomes assessed:
In-semester test (Take-home short release)	Mid-semester test Take home exam	25%	Week 07 Due date: 07 Apr 2022 at 18:00	2 hours
Outcomes assessed:
Assignment	Applied project Online collaboration	25%	Week 13 Due date: 26 May 2022 at 18:00 Closing date: 17 Feb 2022	10-12 pages
Outcomes assessed:
= group assignment ? = Type D final exam ? = Type D 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.

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	Introduction, linear regression with treatment effects	Lecture (3 hr)
Week 02	OLS and GLS in matrix	Lecture and tutorial (3 hr)
Week 03	Endogeneity, instrumental variable method, 2SLS	Lecture and tutorial (3 hr)
Week 04	Pooled OLS, first difference estimator	Lecture and tutorial (3 hr)
Week 05	Fixed-effect and random-effect estimators	Lecture and tutorial (3 hr)
Week 06	Social and natural experiments, difference in differences (DID) estimation	Lecture and tutorial (3 hr)
Week 07	Mid-semester exam	Lecture (3 hr)
Week 08	Modelling issues in panel data	Lecture and tutorial (3 hr)
Week 09	Generalized method of moments (GMM)	Lecture and tutorial (3 hr)
Week 10	Dynamic linear panel data models	Lecture and tutorial (3 hr)
Week 11	Simultaneous equation models for panel data	Lecture and tutorial (3 hr)
Week 12	Discrete Choice Models for Panel Data	Lecture and tutorial (3 hr)
Week 13	Review for final exam	Lecture and tutorial (3 hr)

Attendance and class requirements

Attendance: students 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 which 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 link available on Canvas.

Recommended textbooks:

Econometrics of Panel Data: Methods and Applications, by Erik Biørn, Oxford University Press 2016

Microeconometrics: Methods and Applications, by Cameron and Trivedi, Cambridge University Press 2005

Microeconometrics Using Stata, by Cameron and Trivedi, Stata Press, 2010

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 models and econometric methods extensively used in analysis of panel data
LO2. critically evaluate existing empirical studies of panel data from economics and other fields
LO3. implement empirical projects using panel data and clearly demonstrate the ideas.

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.

Lecture notes, tutorial questions, and applied project have been updated according to the 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.

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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