• Class Number 2594
  • Term Code 3130
  • Class Info
  • Unit Value 6 units
  • Topic On Campus
  • Mode of Delivery In Person
  • COURSE CONVENER
    • Dr Long Chu
  • LECTURER
    • Dr Long Chu
  • Class Dates
  • Class Start Date 22/02/2021
  • Class End Date 28/05/2021
  • Census Date 31/03/2021
  • Last Date to Enrol 01/03/2021
  • TUTOR
    • Thai Nguyen
SELT Survey Results

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This is a Master degree and PhD level course in applied economic dynamics, designed to introduce students to a range of concepts and techniques required for modelling and analysing economic problems. Topics include time-series econometrics, transitional dynamics, optimal control theory and recursive dynamic programming with applications to natural resource economics, ecological dynamics, macroeconomic dynamics and economic growth.  In addition to paper-and-pen analysis, students will also use computers to solve common dynamic problems such as finance planning, infectious disease simulation and optimal fishing problems.

Learning Outcomes

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

  1. Be familiar with a wide range of the mathematical concepts, formalisms and techniques that are commonly used to analyse dynamic structures in economics.
  2. Have confidence in the mathematical techniques required for modelling and analysing dynamic problems in economics.
  3. Be able to simulate/solve some common dynamic models in economics using computers.

Topic 1: Starting thinking dynamically

  1. Hoy, M., Livernois, J., McKenna, C., Rees, R. & Stengos, T. (2011) Mathematics for Economics, 3rd edition, Massachusetts Institute of Technology (Chapter 3: p. 61-95).
  2. Welch, I. Corporate Finance, 2nd edition (Chapters 2-3: p. 11-58).
  3. Chu, L. & Grafton, Q. (2019). Short-term Pain for Long-term Gain: Urban Water Pricing and the Risk-adjusted User Cost, Water Economics and Policy, 1871005,

Topic 2: Discrete-time transitional dynamics

  1. Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc. (Chapter 13: p. 407-449).
  2. Enders, W. (2004) Applied Econometric Time Series, 2nd edition, John Wiley & Sons, Inc. (Chapter 6: p. 317-347).
  3. Granger, C.W.J & Newbold, P. (1974) Spurious Regressions in Econometrics, Journal of Econometrics, 2: 111-120.
  4. Engle, R.E. & Granger, C.W.J. (1987) Cointegration and Error-Correction: Representation, Estimation and Testing, Econometrica, 55: 251-276.

Topic 3: Discrete-time dynamic optimization

  1. Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc (Chapter 15: p. 489-496).
  2. Blanchard, O.J. & Fischer, S. (1989) Lectures on Macroeconomics, Massachusetts Institute of Technology (Chapter 2: p. 37-52).
  3. Romer, D. (2006), Advanced Macroeconomics, 3rd edition, McGraw-Hill Companies (Chapter 7: p. 346-365).
  4. Chow, G.C. (1997) Dynamic Economics Optimization by the Lagrange Method. Oxford University Press (Chapter 2: p. 19-31).

Topic 4: Continuous-time transitional dynamics

  1. Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc. (Chapter 14: p. 451-488).
  2. Kompas, T., Chu, L. & Nguyen, H. (2016) A practical Optimal Surveillance Policy for Invasive Weeds: An Application to Hawkweed in Australia, Ecological Economics, 130: 156 – 165.
  3. Kompas T., Chu, L., Ha, P. & Spring, D. (2019). Budgeting and portfolio allocation for biosecurity measures, Australian Journal of Agricultural and Resource Economics, 63: 412-438
  4. Barro, R. J. & Sala-i-Martin, X. (2004) Economic Growth, 2nd edition, Massachusetts Institute of Technology (Chapter 1: p. 23-61).
  5. Berryman, A. A. (1992) The Origins and Evolution of Predator-Prey Theory, Ecology, 73 (5): 1530-1535.
  6. Keeling, M. J. & Rohani, P. (2007) Modeling Infectious Diseases in Humans and Animals. Princeton University Press (Chapter 2: p. 15-41).

Topic 5: Continuous-time dynamic optimization

  1. Hoy, M., Livernois, J., McKenna, C., Rees, R. & Stengos, T. (2011) Mathematics for Economics, 3rd edition, Massachusetts Institute of Technology. (Chapter 25: p. 845-899).
  2. Barro, R. J. & Sala-i-Martin, X. (2004) Economic Growth, Massachusetts Institute of Technology (Chapter 2: p. 85-121).
  3. Grafton, Q., Kompas, T., Che, N., Chu, L.& Hilborn, R. (2012) BMEY as a Fisheries Management Target, Fish and Fisheries, 13: 303–312.
  4. Grafton, Q., Kompas, T., Chu, L. & Che, N. (2010) Maximum Economic Yield, Special Fisheries Issue (Diane Dupont, guest editor), Australian Journal of Agricultural and Resource Economics, 54: 273-280.

Topic 6: Recursive dynamic programming

  1. Chu, L., Grafton, R. Q., & Stewardson, M. (2018). Resilience, decision-making, and environmental water releases. Earth's Future, 6, 777–792.
  2. Kompas, T. & Chu, L. (2012), Comparing Approximation Techniques to Continuous-Time Stochastic Dynamic Programming Problems: Applications to Natural Resource Modelling, Environmental Modelling & Software, 38:1-12.
  3. Chu, L., Kompas, T. & Grafton, Q. (2015) Impulse Controls and Uncertainty in Economics, Environmental Modelling and Software, 65: 50-57
  4. Adda, J. & Cooper, R. (2003) Dynamic Economics Quantitative Methods and Applications, Massachusetts Institute of Technology (Chapter 2: p. 7-24).

Staff Feedback

Students will be given feedback in the following forms in this course:
  • Written comments
  • Verbal comments
  • Feedback to the whole class, to groups, to individuals, focus groups

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 Weeks 1-2: Topic 1: Start thinking dynamically and the shooting method Indicative schedule only, subject to the actual pace of the class. An introductory session will be included.
2 Weeks 3-4: Topic 2: Discrete-time transitional dynamics Indicative schedule only, subject to the actual pace of the class.
3 Weeks 5-6: Topic 3: Discrete-time dynamic optimization Indicative schedule only, subject to the actual pace of the class.
4 Weeks 7-8: Topic 4: Continuous-time transitional dynamics Indicative schedule only, subject to the actual pace of the class.
5 Weeks 9-10: Topic 5: Continuous-time dynamic optimizations Indicative schedule only, subject to the actual pace of the class.
6 Weeks 11-12: Topic 6: Recursive dynamic programming Indicative schedule only, subject to the actual pace of the class.

Assessment Summary

Assessment task Value Due Date Learning Outcomes
Quiz 1 15 % 10/03/2021 1, 2, 3
Quiz 2 15 % 20/04/2021 1, 2, 3
Quiz 3 15 % 20/05/2021 1, 2, 3
Final exam 55 % 10/06/2021 1, 2, 3

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

Policies

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: 15 %
Due Date: 10/03/2021
Learning Outcomes: 1, 2, 3

Quiz 1

Computer-based; Two attempts allowed; Multiple choice format; Students are free to discuss. Covering the introductory session, the prerequisites and topic 1.

Assessment Task 2

Value: 15 %
Due Date: 20/04/2021
Learning Outcomes: 1, 2, 3

Quiz 2

Computer-based; Two attempts allowed; Multiple choice format; Students are free to discuss. Covering topics 2 and 3.

Assessment Task 3

Value: 15 %
Due Date: 20/05/2021
Learning Outcomes: 1, 2, 3

Quiz 3

Non-redemptive; Computer-based; Two attempts allowed; Multiple choice format; Students are free to discuss. Covering topics 4, 5 and 6

Assessment Task 4

Value: 55 %
Due Date: 10/06/2021
Learning Outcomes: 1, 2, 3

Final exam

Computer-based; One attempt allowed; 30-minute reading and 180-minute attempt; Students are NOT allowed to discuss (plagiarism rules will apply). Exact time and place will be arranged by the University. Covering all topics of the course.

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

The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

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 Long Chu
02 6125 6557
long.chu@anu.edu.au

Research Interests


Dr Long Chu

Dr Long Chu
02 6125 0093
long.chu@anu.edu.au

Research Interests


Dr Long Chu

Thai Nguyen
Thai.Nguyen@anu.edu.au

Research Interests


Thai Nguyen

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