• Class Number 9225
  • Term Code 2960
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • AsPr Fedor Iskhakov
  • Class Dates
  • Class Start Date 22/07/2019
  • Class End Date 25/10/2019
  • Census Date 31/08/2019
  • Last Date to Enrol 29/07/2019
SELT Survey Results

This course will teach the basics of programming and computational skills for economic analysis and enable the students to take numerical approach to familiar mathematical problems. Students will learn to graphically represent familiar ideas such as supply and demand curves, equilibrium prices and consumer choice. They will explore how these choices and equilibria change with shifts in policy instruments, preferences and technologies. In the process they willlearn to use common computational solution methods, such as root finding and optimization. Students will also learn how to obtain, manipulate and represent data, using tools such as scatterplots and histograms.

Learning Outcomes

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

1. Basic programming skills (conditions, loops, flow control, iteration, etc.)
2. Ability to implement familiar mathematical methods on a computer
3. Reinforcement of key ideas from economic analysis
4. Algorithm and data manipulation and visualization of economic data

Research-Led Teaching

Lectures and practical exercises cover the work recently published in the leading international economics journals.

Kevin Sheppard "Introduction to Python for Econometrics, Statistics and Data Analysis." 3rd Edition University of Oxford Thursday 1st February, 2018 (downloadable at https://www.kevinsheppard.com/Python_for_Econometrics)

Thomas Sargent and John Stachurski "Lectures in Quantitative Economics" (online resource) 

Jérôme Adda and Russell Cooper "Dynamic Economics. Quantitative Methods and Applications." MIT Press, 2003 (available at Chifley library on 2-hour reserve)

Staff Feedback

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

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

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.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction, Binary numbers Weekly assignment
2 Python basics, object oriented programming Weekly assignment
3 Algorithms and complexity Weekly assignment
4 Data manipulation and visualization Weekly assignment
5 Linear algebra and linear programming Weekly assignment
6 Dynamic programming in discrete world Midterm Assignment
7 Function approximation and interpolation Weekly assignment
8 Dynamic programming with continuous choice Weekly assignment
9 Stochastic models, quadrature and integration Weekly assignment
10 Random numbers and simulations, Monte Carlo Weekly assignment
11 Equilibrium models Weekly assignment
12 Structural estimation Weekly assignment

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Weekly assignments 0 % 26/07/2019 30/10/2019 1,2,3,4
Midterm Assignment 40 % 16/09/2019 30/09/2019 1,2,3,4
Final exam 60 % 16/11/2019 04/12/2019 1,2,3,4

* 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. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

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.


This is an on-campus course. Attendance at all teaching events, while not compulsory, is expected in line

with “Code of Practice for Teaching and Learning”, clause 2 paragraph (b).

 In addition, tutorials are a discussion-based class. Providing worked solutions would not effectively

compensate for missing a tutorial. Students who, through unavoidable and unplanned occurrences, are

unable to attend a tutorial class one week are encouraged to work through the problems and attend a

consultation session for discussion and solutions.


Open book exam in computer lab, 3 hours. Further information will be given at least 2 weeks prior to examination week.

Assessment Task 1

Value: 0 %
Due Date: 26/07/2019
Return of Assessment: 30/10/2019
Learning Outcomes: 1,2,3,4

Weekly assignments

Small exercises in the end of each lecture to give you an opportunity to try implementation of the material from the lecture. Intended to be solved individually or in groups during the week. Will be discussed in the lab that follows the lecture, and serve as building blocks in the larger applications. Students who choose to turn in the weekly assignments before the lab will receive feedback on their work during the following week.

Assessment Task 2

Value: 40 %
Due Date: 16/09/2019
Return of Assessment: 30/09/2019
Learning Outcomes: 1,2,3,4

Midterm Assignment

The midterm assignment involves coding tasks implementing simple economic models and are to be performed in groups of 2-3 people. Groups will be formed during weeks 4 and 5 during the class, and posted on Wattle. The assignments will be graded for correct implementation of the economic model, but also for code style and proper use of version control tools. Several models will be offered for implementation, each accompanied with a set of tasks to perform. All models will be assigned to groups on the first come first served (FIFO) basis before any model can be assigned another time. Groups will claim the models by email, or in person during the class on week 6, two weeks prior to due date. Ten percent (10%) of the individual grades will be based on the feedback from within the group on the contribution of each group member. More details will be given in class.

Assessment Task 3

Value: 60 %
Due Date: 16/11/2019
Return of Assessment: 04/12/2019
Learning Outcomes: 1,2,3,4

Final exam

3 hours open book exam in the computer lab. Use of own laptops is permitted (provided that the needed software is installed), the computers in the lab will have the necessary software. The final exam will contain several short answer questions (knowing the facts), several middle size questions (find a bug in the code) and several small individual coding tasks (write your code). The final exam will cover material presented throughout the semester and will be held during the University examination period. Internet access during the exam is permitted.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to 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 be familiar with the academic integrity principle and Academic Misconduct Rule, 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 Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

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) 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

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.
  • Late submission permitted. 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).

AsPr Fedor Iskhakov

Research Interests

AsPr Fedor Iskhakov

Monday 11:00 12:00

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