ECMT2950: Semester 1, 2022

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Units ECMT2950 Semester 1 2022 [Normal day]

Unit of study_

ECMT2950: Intermediate Econometrics - Advanced

Semester 1, 2022 [Normal day] - Remote

Overview

This unit provides a thorough introduction to the econometrics of cross-section and panel data. We begin with a detailed discussion of the assumptions and statistical properties of the multiple linear regression model. We explore the econometric methods available when these assumptions do not hold. More specifically, we cover linear probability models, heteroscedasticity and GLS, omitted variable bias, measurement error, instrumental variables, quantile regression and models for pooled cross-section and panel data well-suited to policy analysis and the estimation of treatment effects. Throughout, we discuss economic applications and utilise practical computer applications where appropriate.

Unit details and rules

Unit code	ECMT2950
Academic unit	Economics
Credit points	6
Prohibitions ?	ECMT2110 or ECMT2150
Prerequisites ?	(A minimum of 65% in (ECMT1010 or MATH1905 or MATH1005 or MATH1015 or DATA1001 or DATA1901 or ENVX1002)) and (a minimum of 65% in (ECMT1020 or MATH1002 or MATH1902 or DATA1002 or DATA1903)) or (a minimum of 65% in BUSS1020)
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Rebecca Edwards, rebecca.edwards@sydney.edu.au
Tutor(s)	Felipe Queiroz Pelaio, felipe.queirozpelaio@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final Exam Final Exam. Type C.	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Problem style questions & use of regression software to solve problems.	10%	Week 04 Due date: 20 Mar 2022 at 23:59	TBA
Outcomes assessed:
In-semester test (Open book)	Mid-semester exam Mid-semester exam. Type C exam.	25%	Week 07 Due date: 05 Apr 2022 at 14:00	1 hour
Outcomes assessed:
Assignment	Assignment 2 Empirical analysis project with short written response	15%	Week 12 Due date: 22 May 2022 at 23:59	TBA
Outcomes assessed:
= Type C final exam ? = Type C in-semester exam ?

Assessment summary

Detailed information for each assessment can be found 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

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.

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:

Assignment 1 and Assignment 2 have a maximum extension of 10 calendar days in order to accommodate the timely return of marked assignments. Work not submitted on time will be subject to a late penalty in accordance with FASS policy.

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.

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 to Econometrics. Multiple Linear Regression & Deriving OLS I	Lecture and tutorial (3 hr)
Week 02	Multiple Linear Regression – Assumptions & Properties & Deriving OLS II	Lecture and tutorial (3 hr)
Week 03	Multiple Linear Regression – Assumptions & Properties, continued. Inference	Lecture and tutorial (3 hr)
Week 04	Inference, continued. Asymptotic properties of OLS. Dummy variables and the Linear Probability Model	Lecture and tutorial (3 hr)
Week 05	Specification Issues I	Lecture and tutorial (3 hr)
Week 06	Specification Issues II - Heteroskedasticity, GLS, FGLS	Lecture and tutorial (3 hr)
Week 07	Specification Issues II - Heteroskedasticity, GLS, FGLS	Tutorial (1 hr)
Week 08	Median and Quantile Regression	Lecture and tutorial (3 hr)
Week 09	Instrumental Variables	Lecture and tutorial (3 hr)
Week 10	Instrumental Variables, continued	Lecture and tutorial (3 hr)
Week 11	Treatment Effects & Panel Data Models	Lecture and tutorial (3 hr)
Week 12	Treatment Effects & Panel Data Models, continued	Lecture and tutorial (3 hr)
Week 13	Treatment Effects & Panel Data Models, continued. Review	Lecture and tutorial (3 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

Required text: Wooldridge, J. M., Introductory Econometrics, A Modern Approach, South-Western Cengage Learning.

Please note the latest edition is the 7th edition. You can use that or the 6th Edition.

Additional required readings for this unit can be accessed from the Library via the Reading List link available in the Canvas site for this unit.

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. Describe and explain the assumptions and statistical properties of the linear regression model and understand when these assumptions may not hold
LO2. Implement a variety of econometric tools for causal inference
LO3. Identify appropriate econometric techniques to implement when key assumptions for the linear regression model do not hold
LO4. Conduct and critically evaluate hypothesis testing
LO5. Demonstrate an understanding of and critically evaluate applied economic research
LO6. Use econometric software for econometric modelling

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.

This is a relatively new unit of study, developed with an explicit aim to challenge students to develop a deeper understanding of the econometrics of cross-section and panel data than covered in ECMT2150. It has been developed and is reviewed and refined each year in light of student feedback and consultation with colleagues.

Additional information

Computer Software: STATA

Throughout this unit you will be required to use a computer and specialised econometric software. The statistics and data analysis program STATA will be taught as part of this unit, and will be demonstrated during the lectures and tutorials.

Accessing STATA:

This software is available to students enrolled in this course in the teaching labs, the learning hubs around campus and on your own devices via the UniConnect Cloud.
You can, but it is not necessary to, purchase your own STATA
See https://surveydesign.com.au/buystudent.html. A 6-month licence to the latest version of STATA/BE is $76. This version of Stata will be sufficient for any tutorial or assignment in this unit.

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

Additional information

Additional information

Disclaimer

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