- Class Number 8368
- Term Code 3060
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Prof Fedor Iskhakov
- Class Dates
- Class Start Date 27/07/2020
- Class End Date 30/10/2020
- Census Date 31/08/2020
- Last Date to Enrol 03/08/2020
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.
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
Lectures and practical exercises cover the work recently published in the leading international economics journals.
Examination Material or equipment
Open book exam, personal computers and any other resources are allowed in the exam
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)
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
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.
|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 task||Value||Due Date||Return of assessment||Learning Outcomes|
|Weekly assignments (zero value in final grade)||0 %||27/07/2020||30/10/2020||1,2,3,4|
|Midterm Assignment||40 %||21/09/2020||05/10/2020||1,2,4|
|Final exam||60 %||09/11/2020||23/11/2020||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:
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.
The course will be delivered remotely through screen recordings and live Q&A sessions.
Open book exam on computers, 3 hours. Further information will be given at least 2 weeks prior to examination week.
Assessment Task 1
Learning Outcomes: 1,2,3,4
Weekly assignments (zero value in final grade)
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 and submitted online (via the GitHub platform) before the next week's tutorials. Will be discussed during the Q&A session in the following week, and serve as building blocks in the larger applications. Students who choose to turn in the weekly assignments within 7 days will receive feedback on their work during the following week. More details will be given in week 1 of lectures.
Assessment Task 2
Learning Outcomes: 1,2,4
The midterm assignment involves coding tasks to implement simple economic models, and are to be performed in groups of 2-3 people (via zoom). Groups will be formed by the convener in week 2-3 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 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 no later than week 4 of lectures.
Assessment Task 3
Learning Outcomes: 1,2,3,4
3 hours open book exam on your personal computer. 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. More details will be given no later than week 8 of lectures.
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.
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.
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.
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.
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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.
Distribution of grades policy
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