• Class Number 4755
  • Term Code 2930
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Nicholas Biddle
    • Benjamin Phillips
    • Dr Jilu Zhang
  • Class Dates
  • Class Start Date 25/02/2019
  • Class End Date 31/05/2019
  • Census Date 31/03/2019
  • Last Date to Enrol 04/03/2019
SELT Survey Results

The philosophy of Case Studies is that the best way to learn how to research is to do research. Students conduct, under the supervision of faculty, their own research projects, culminating in the writing of a research paper. Lectures will be given on a selection of topics that can prove useful in research.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. formulation of research objective
  2. execution of a research agenda to adress the research objective
  3. a demonstration of your competency in the research methods your objective mandates
  4. writing a research paper
  5. presentation of the research project

Research-Led Teaching

This course has a heavy research focus. Students will be required to replicate existing applied econometric research on real data, and extend this research using their own research questions and ideas.

Required Resources

Students will be given a list of required readings in the first lecture of the course. This will include (a) texts that summarise the main techniques used in the course (available online through the ANU Library) (b) between 4-6 compulsory case-studies of applied econometrics that will be used in lectures and (c) between 4-6 case-studies that will form the basis of student assessment

Staff Feedback

Students will be given feedback in the following forms in this course:

  • Worked answers for Quiz 1 and 2
  • Verbal feedback on Research presentation
  • Written feedback on Research report
  • Computer Lab worked examples

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Course Format

Lecture:  Tuesday 15:00 to 16:00, CBE Lecture Theatre 3

Computer Lab: Either Tuesday 09:00 to 11:00 OR Tuesday 11:00 to 13:00. Additional computer labs may be added based on student numbers.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to course During this first lecture, we will introduce the content of the course and discuss different types of data that can be used in applied econometrics. We will talk about the concept of an independent and a dependent variable, and different ways to analyse data. We will introduce the potential topics for student assignments, and discuss how to access data No computer Lab
2 Continuous Data I During the lecture, we will introduce the main techniques for analysing individual-level data for continuous dependent variables. This includes a discussion of univariate and bivariate analysis, as well as simple linear regression. No computer Lab
3 Continuous Data II We will continue the discussion of analysis of continuous dependent variables by looking at multiple linear regression (when there is one dependent variable and more than one independent variable). We will discuss the assumptions underlying linear regression, and the different ways in which they can be violated. Computer Lab - Simple Linear Regression Assessment Task 1 due
4 Categorical Data I During the lecture for this week, we will discuss one of the main extensions to micro-econometrics, the use of non-continuous dependent variables. We will begin by looking at binary dependent variables (yes/no) and how to calculate and interpret odds ratios and marginal effects in Logit/Probit regression. Computer Lab - Multiple linear regression and dummy variables
5 Categorical Data II We will extend our analysis of categorical dependent variables by looking at instances of more than two categories. This includes multinomial Logit/Probit; ordered Logit/Probit; and count data Computer Lab - Logit/Probit and calculation of predicted probabilities
6 Time Series I We will switch the focus of our analysis during this lecture, and look at the analysis of aggregate data, with a particular focus on time series analysis. We will discuss the concepts of (and main techniques for) lags, stationarity, and structural breaks. Computer Lab - Multinomial Logit/Probit
7 Presenting Data I For the next three lectures we will change our focus from the main techniques of econometric analysis to the presentation of data. During the first of the lectures on data presentation, we will discuss some of the practicalities of summarising data for an academic or policy audience. We will discuss in detail the requirements for the research project. Computer Lab - Ordered Logit/Probit Assessment Task 2 due
8 Presenting Data II During this week's lecture, we will discuss two examples of applied econometrics. Students will be expected to have read the papers beforehand, and we will discuss: (a) the choice of data (b) the choice of techniques (c) the presentation of results (d) the implications of the findings Computer Lab - Poisson and Negative Binomial regression
9 Presenting Data III We will discuss two additional examples of applied econometrics, following the same format as Week 8 Computer Lab - Basic time series analysis
10 Presenting Data IV There will be no lecture or computer labs during Week 10. Students will be required to give a 5 minute presentation on their project, with 5 minutes of Questions and Answers Assessment Task 3 due
11 Time Series II We will extend our analysis of 'time', by looking at the main techniques for panel data analysis. That is, when we have more than one observation, for more than one individual. We will begin our discussion of panel data by discussing pooled regression and differencing. Computer Lab - Assistance with case studies project
12 Time Series III We will extend our discussion of panel data by discussing fixed and random effects models. Computer Lab - Assistance with case studies project Assessment Task 4 due

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Online Quiz 1 5 % 15/03/2019 29/03/2019 3
Online Quiz 2 15 % 26/04/2019 10/05/2019 3
Research presentation 10 % 14/05/2019 17/05/2019 1, 2
Research report 45 % 31/05/2019 21/06/2019 2, 3, 4, 5
Online Quiz 3 25 % 21/06/2019 04/07/2019 4, 5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 5 %
Due Date: 15/03/2019
Return of Assessment: 29/03/2019
Learning Outcomes: 3

Online Quiz 1

Student will undertake a quiz through Wattle based on the content covered in Weeks 1 to 3. Students will have one-week to complete the quiz online before the due date (through Wattle) and will consist of a combination of multiple choice and short answer questions. Students will be required to explain concepts, interpret econometric results, and undertake basic analysis. It is expected that the quiz will have 3 questions.

Assessment Task 2

Value: 15 %
Due Date: 26/04/2019
Return of Assessment: 10/05/2019
Learning Outcomes: 3

Online Quiz 2

Student will undertake a quiz through Wattle based on the content covered in Weeks 4 to 6. Students will have one-week to complete the quiz online before the due date (through Wattle) and will consist of a combination of multiple choice and short answer questions. Students will be required to explain concepts, interpret econometric results, and undertake basic analysis. It is expected that the quiz will have 7-10 questions.

Assessment Task 3

Value: 10 %
Due Date: 14/05/2019
Return of Assessment: 17/05/2019
Learning Outcomes: 1, 2

Research presentation

Students will give a five minute presentation on their own independent research. Students will be able to choose one of six research reports that they will be required to replicate and make a minor extension to. Students will give the presentations during Week 10 during the time allocated to the computer lab and lecture. It is expected that students will participate in and comment on the presentations of other students.

More details will be given in Week 01 and Week 07

Assessment Task 4

Value: 45 %
Due Date: 31/05/2019
Return of Assessment: 21/06/2019
Learning Outcomes: 2, 3, 4, 5

Research report

Students will submit a 4,000-5,000 word essay based on their independent research. Students will be able to choose one of six research reports that they will be required to replicate and make a minor extension to. More details will be given in Week 01 and Week 07

Assessment Task 5

Value: 25 %
Due Date: 21/06/2019
Return of Assessment: 04/07/2019
Learning Outcomes: 4, 5

Online Quiz 3

Student will undertake a quiz through Wattle based on the content covered in the entirety of the course. Students will have two weeks to complete the quiz. The quiz will consist of a combination of multiple choice and short answer questions, as well as a short essay based on an additional set of applied econometric readings (provided at the end of the course through Wattle). Students will be required to explain concepts, interpret econometric results, and undertake basic analysis

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) as submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Nicholas Biddle
02 6125 1301

Research Interests


Dr Nicholas Biddle

Tuesday 13:00 15:00
Benjamin Phillips
6125 1301

Research Interests

Benjamin Phillips

Tuesday 13:00 15:00
Dr Jilu Zhang
6125 1301

Research Interests

Dr Jilu Zhang

Thursday 14:00 16:00

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions