ECON5403 Econometric Theory and Methods - 2020

Term 3
6 Units of Credit
The course outline is not available for current term. To view outlines from other years and/or terms, visit the archives .

1. Course Details

Summary of Course

The course provides unifying estimation methods, inference and computation for a variety of single equation econometric models and gives some theoretical justification for the methods. The course emphasises the links between the theory for econometric models, the computations required for inference, and the application of the models to real examples. Being equipped with this knowledge will enable students to conduct a broad range of relatively sophisticated econometric modelling tasks. Many of the underlying methods taught in the course and the reasoning behind the methods also apply to more sophisticated models and applications.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

Course Aims and Relationship to Other Courses


The course aims to give students an understanding of modern econometric methods and a heuristic theoretical justification of their use at an intermediate level. The methods are useful in a range of business applications including applied economics and finance. The course will teach you about the methodology, you will get an intuitive understanding of the theory and ideas behind the methodology, and you will learn how to apply the methods to data using the Stata computer package. By the end of the course you should be able to start reading some of the academic literature employing modern econometric methods to address business problems.

The assignment work to be handed in will train you to express econometric ideas clearly and concisely and to work in a team. For some of the assignment work, students will be expected to write a report that summarises the essence of the findings of a piece of research and the importance of those findings, with analytical details presented in an appendix.

Students will learn to analyse data and report results based on the evidence at hand and report the appropriate uncertainty in the results. The emphasis is on students carrying out analyses at an intermediate level.

Relationship to other courses

The course will develop ideas from first principles, but students are expected to have knowledge of elementary econometrics and have analytical skills and training equivalent to those of the prerequisite course (ECON3209 or MATH2801 or MATH2901). Students should have some familiarity with matrix algebra and an understanding of probability distributions and estimation. The course will give students sound preparation for courses that use applied econometrics as well as a sound basis for doing honours.

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeProfRobert KohnRoom 434, UNSW Business School9385 1089Monday and Friday by appointment; Tuesday 11am-12pm, Wednesday 12-1pm; Thursday 12-1pm

Communication with staff

You should feel free to contact me about any academic matter. However, I strongly encourage you to make enquiries about the subject material during the lectures or tutorials or during consultation time. Discussion of course subject material will not be entered into via lengthy emails.

Email correspondence on administrative matters (e.g., advising inability to attend tutorial) will be responded to within 48 hours, but not over weekends. Please note that I have no advance notice of the date and time of the exam (the subject of many emails).

Please email me through my regular email address rather than Moodle.

Student Enrolment Requests

Students can vary their own enrolment (including switching lecture streams or tutorials) via myUNSW until the end of Week 1. In general, most other student enrolment requests should be directed to The Nucleus: Student Hub (formerly Student Central). These include enrolment in full courses or tutorials, course timetable clashes, waiving prerequisites for any course, transfer-of-credit (international exchange, transfer to UNSW, cross-institutional study, etc.), or any other request which requires a decision about equivalence of courses and late enrolment for any course. Where appropriate, the request will be passed to the School Office for approval before processing. Note that enrolment changes are rarely considered after Week 2 classes have taken place.

3. Learning and Teaching Activities

Approach to Learning and Teaching in the Course

Learning will be through:

  • Weekly lectures and tutorial classes.
  • The lecture notes made available on Moodle. These form the basis of the lectures.
  • Assigned reading supplementing the lectures. These consist of additional notes posted on Moodle, readings from chapters in the assigned text book as well as material from other sources.
  • Feedback on the assessment items, which will be discussed during tutorials.

Learning Activities and Teaching Strategies


Lectures will be delivered live/synchronously online at the times in the UNSW Timetable. The lectures will include explanation of methodology, practice, and some of the theory of the topics in the course. However, the student should not regard the lecture contents as exhaustive.

It is important for students to devote a considerable amount of time to private study to achieve an appropriate level of understanding and to practice the different econometric tools introduced. Lectures provide one of the principal means of learning instruction, but it is essential that their contribution be bolstered and supported by other learning resources.

The examinable content of the course is defined by the references given in the lecture schedule, the lecture content, and the content of the tutorial program.


The more you read the more you know, but the more you practice the more you learn and understand. So the key to the understanding of this course is problem solving.

Discussion will be based around a sequence of problem sets. You are expected to make a serious attempt at all questions on an exercise sheet before attending the tutorial at which it is discussed. It may not be possible to discuss all the questions on the problem sets in the allotted time and you should not expect all questions to be solved in depth at the tutorials.

The purpose of tutorials is to enable you to raise questions about difficult topics or problems encountered. Students must not expect another lecture, but must come prepared with questions of their own.

Every tutorial, several students may be randomly chosen to discuss their attempts to answer the tutorial problems. The aim is to encourage discussion within the classroom and solve the issues you and your classmates have encountered with the problems.

Out-of-Class Study

Most learning will usually be achieved outside of class time. Lectures can only provide a structure to assist your study, and tutorial time is limited.

You are encouraged to purchase the recommended textbook for this course (see Course Resources section) and read the assigned chapters. My advice to you is, make the book your friend and use the consultation time to come and ask for help understanding what you read.

5. Course Resources

The website for this course is on UNSW Moodle.

Lecture Notes

These are comprehensive and will usually be available on Moodle before the lecture. Note however that there may be additional discussion of concepts in class beyond the notes that you should endeavour to understand.

Recommended text

  • Introduction to Econometrics. J.H. Stock and M.W. Watson. 4th edition, Global edition. Pearson.

This is available from the UNSW Bookshop or the UNSW Library.

Other useful references (these are not necessarily available from UNSW Bookshop)

  • Wooldridge, J. M. Introductory Econometrics: a modern approach. 6th edition. This is also a very good book with similar aims to Stock and Watson.
  • Sheather, S.J. 2009 A Modern Approach to Regression with R. Springer. This book is very good for regression examples and diagnostics.
  • Greene, W.H. 2012 Econometric Analysis, 7th edition. Pearson International Books. (this book is probably a little too theoretical for most students).


The Stata package will be used for computation and can be accessed via UNSW myAccess. To use access Stata from your computer, please go to and install the Citrix Receiver following the instructions on the site. Once you have installed the software, go back to the myAccess site and click on “Access my apps” at the top of the page. Log in with your zID and password and choose Stata from the list of software. Please make sure that your browser does not block pop-ups from the website.

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

​The School of Economics strives to be responsive to student feedback. If you would like more information on how the design of this course and changes made to it over time have taken students’ needs and preferences into account, please contact the Director of Education at the School of Economics.

​Consent for De-Identified Data to be Used for Secondary Research into Improving Student Experience

To enhance your student experience, researchers at UNSW conduct academic research that involves the use of de-identified student data, such as assessment outcomes, course grades, course engagement and participation, etc. Students of this course are being invited to provide their consent for their de-identified data to be shared with UNSW researchers for research purposes after the course is completed.

Providing consent for your de-identified data to be used in academic research is voluntary and not doing so will not have an impact on your course grades.

Researchers who want to access your de-identified data for future research projects will need to submit individual UNSW Ethics Applications for approval before they can access your data.

A full description of the research activities aims, risks associated with these activities and how your privacy and confidentiality will be protected at all times can be found here.

If you consent to have your de-identified data used for academic research into improving student experience, you do not need to do anything. Your consent will be implied, and your data may be used for research in a format that will not individually identify you after the course is completed.

If you do not consent for this to happen, please email the opt-out form to to opt-out from having your de-identified data used in this manner. If you complete the opt-out form, the information about you that was collected during this course will not be used in academic research.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit:
Week Activity Topic Assessment/Other
Week 1: 14th SeptemberLecture
  • Review of the simple linear regression model
  • Class exercises


  • Lecture notes: please browse before lecture
  • Review chapters 1 to 3 and 5 of Stock and Watson
  • Assigned readings
  • Continue review of simple linear regression
  • Class exercises
  • Lecture notes: please browse before lecture
  • Review chapters 1 to 3 and 5 of Stock and Watson
  • Assigned readings
  • Introduction / review of Stata commands
  • Go over some background material
  • Class exercises
Week 2: 21st SeptemberLecture
  • Review of some theory for the simple linear regression model
  • Class exercises


  • Lecture notes: please browse before lecture
  • Stock and Watson: chapter 5
  • Assigned readings
  • Continue reviewing theory for simple linear regression model
  • Class exercises


  • Lecture notes: please browse before lecture
  • Stock and Watson: chapter 5
  • Assigned readings
  • Discuss lectures
  • Discuss new Stata commands
  • Discuss solution to some assigned problems
  • Problem set 2 will be available at or before 5pm on Friday
Week 3: 28th SeptemberLecture
  • Transformation of the dependent and independent variables in linear regression
  • Discuss heteroscedastiicty


  • Lecture notes: please browse before lecture
  • Stock and Watson: read parts of chapters 5 to 8, chapter 9
  • Assigned readings


  • Box Cox transformations of the dependent and independent variables
  • More regression topics: dummy variables, interactions, elasticity
  • Lecture notes: please browse before lecture
  • Stock and Watson: Read parts of chapters 5 to 8, chapter 9
  • Assigned readings


  • Go over new Stata commands
  • Go over some solutions to problems
  • Discuss material in lectures
  • Problem set 2 will be due at or before 5pm on Friday
Week 4: 5th OctoberLecture
  • Introduction to cross validation to choose the transformation of the dependent variable
  • Class exercises



  • Lecture notes: please browse before lecture
  • Assigned readings



  • Further topics in linear regression interactions; dummy variables; elasticity
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Go over Stata commands
  • Go over solutions to some assigned questions
  • Problem set 4 will be available at or before 5pm on Friday
Week 5 : 12th OctoberLecture
  • Stepwise and all subset regression
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Maximum likelihood estimation for linear regression model
  • Maximum likelihood estimation for other models
  • Maximum likelihood estimation for Box - Cox transformation (time permitting)
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Discuss Stata commands; in particular for stepwise and all subset regression
  • Review solutions to previusly assigned problems
  • Problem set 4 will be due at or before 5pm on Friday
Week 6 FLEXIBILITY WEEK: 19th OctoberLecture

Q & A session 3:00 pm to 4:00 pm




  • Release practice mid-term quiz on midday Monday of this week



Q & A session 2:30 pm to 3:30 pm

  • Go over practice mid-term quiz

Q & A with tutor

  • Problem set 6 will be available at or before 5pm on Friday
Week 7: 26th OctoberLecture
  • Simulation
  • Parametric and nonparametric bootstrap
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Parametric and nonparametric bootstrap
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Discuss Stata for Simulation and the bootstrap
  • Go over solution to some questions set previously
  • Assignment will be available at or before 5pm on Friday
  • Problem set 6 will be due at or before 5pm on Friday
  • Problem set 7 will be available at or before 5pm on Friday
Week 8 : 2nd NovemberLecture
  • Large sample properties for linear regression
  • Robust standard errors
  • Class exercises




  • Lecture notes: please browse before lecture
  • Stock and Watson: chapters 18 and 19
  • Assigned readings




  • Parametric bootstrapping in regression
  • Class exercises
  • Lecture notes: please browse before lecture
  • Assigned readings
  • Stats commands for bootstrap in regression
  • Go over solutions to some assigned problems
  • Problem set 7 will be due at or before 5pm on Friday
Week 9: 9th NovemberLecture
  • Nonparametric bootstrap for linear regression
  • Class exercises



  • Lecture notes: please browse before lecture
  • Reading from and Stock and Watson will be assigned
  • Other assigned reading


  • Binary regression
  • Multinomial regression
  • Cassification
  • ROC curve
  • Class exercises
  • Lecture notes: please browse before lecture
  • Stock and Watson: chapter 11
  • Other assigned reading
  • Stata code for nonparametric bootstrap
  • Stata code for binary and multinomial regression and ROC curves
  • Assignment will be due at or before 5pm on Friday
Week 10: 16th NovemberLecture
  • Bootstrap for binary and multinomial regression
  • Class exercises
  • Lecture notes: please browse before lecture
  • Other assigned reading
  • Stock and Watson: chapter 11
  • Likelihood cross validation for binary regression
  • Class exercises
  • Lecture notes: please browse before lecture
  • Other assigned reading
  • Stata for bootstrap and likelihood cross validation fior binary data
  • Go over solutions to some assigned problems

8. Policies and Support

Information about UNSW Business School protocols, University policies, student responsibilities and education quality and support.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.

These PLOs relate to undergraduate and postgraduate coursework programs.  Separate PLOs for honours and postgraduate research programs are included under 'Related Documents'.

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.



UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Centre.

Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.


UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz:


The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at:

For UNSW policies, penalties, and information to help you avoid plagiarism see: as well as the guidelines in the online ELISE tutorials for all new UNSW students: For information on student conduct see:

For information on how to acknowledge your sources and reference correctly, see: If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.

Student Responsibilities and Conduct

​Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.


It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload


Your regular and punctual attendance at lectures and seminars or in online learning activities is expected in this course. The Business School reserves the right to refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g., tutorials, flipped classroom sessions, seminars, labs, etc.).

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.

Student Support and Resources

​The University and the Business School provide a wide range of support services and resources for students, including:

Business School EQS Consultation Program
The Consultation Program offers academic writing, literacy and numeracy consultations, study skills, exam preparation for Business students. Services include workshops, online resources, individual and group consultations. 
Level 1, Room 1035, Quadrangle Building.
02 9385 4508

Communication Resources
The Business School Communication and Academic Support programs provide online modules, communication workshops and additional online resources to assist you in developing your academic writing.

Business School Student Centre
The Business School Student Centre provides advice and direction on all aspects of admission, enrolment and graduation.
Level 1, Room 1028 in the Quadrangle Building
02 9385 3189

UNSW Learning & Careers Hub
The UNSW Learning & Careers Hub provides academic skills and careers support services—including workshops, individual consultations and a range of online resources—for all UNSW students. See their website for details.
Lower Ground Floor, North Wing Chancellery Building.
02 9385 2060

Student Support Advisors
Student Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process.
John Goodsell Building, Ground Floor.
02 9385 4734

International Student Support
The International Student Experience Unit (ISEU) is the first point of contact for international students. ISEU staff are always here to help with personalised advice and information about all aspects of university life and life in Australia.
Advisors can support you with your student visa, health and wellbeing, making friends, accommodation and academic performance.
02 9385 4734

Equitable Learning Services
Equitable Learning Services (formerly Disability Support Services) is a free and confidential service that provides practical support to ensure that your health condition doesn't adversely affect your studies. Register with the service to receive educational adjustments.
Ground Floor, John Goodsell Building.
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
02 9385 5418

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9385 2650

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
02 9385 3331

UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
02 9385 1333