- Class Number 4539
- Term Code 3130
- Class Info
- Unit Value 6 units
- Topic Online
- Mode of Delivery Online
- Dr Long Chu
- 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
- Thai Nguyen
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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.
Upon successful completion, students will have the knowledge and skills to:
- Be familiar with a wide range of the mathematical concepts, formalisms and techniques that are commonly used to analyse dynamic structures in economics.
- Have confidence in the mathematical techniques required for modelling and analysing dynamic problems in economics.
- Be able to simulate/solve some common dynamic models in economics using computers.
Topic 1: Starting thinking dynamically
- 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).
- Welch, I. Corporate Finance, 2nd edition (Chapters 2-3: p. 11-58).
- 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
- Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc. (Chapter 13: p. 407-449).
- Enders, W. (2004) Applied Econometric Time Series, 2nd edition, John Wiley & Sons, Inc. (Chapter 6: p. 317-347).
- Granger, C.W.J & Newbold, P. (1974) Spurious Regressions in Econometrics, Journal of Econometrics, 2: 111-120.
- 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
- Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc (Chapter 15: p. 489-496).
- Blanchard, O.J. & Fischer, S. (1989) Lectures on Macroeconomics, Massachusetts Institute of Technology (Chapter 2: p. 37-52).
- Romer, D. (2006), Advanced Macroeconomics, 3rd edition, McGraw-Hill Companies (Chapter 7: p. 346-365).
- 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
- Klein, M.W. (2002) Mathematical Methods for Economics, 2nd edition, Pearson Education, Inc. (Chapter 14: p. 451-488).
- 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.
- 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
- Barro, R. J. & Sala-i-Martin, X. (2004) Economic Growth, 2nd edition, Massachusetts Institute of Technology (Chapter 1: p. 23-61).
- Berryman, A. A. (1992) The Origins and Evolution of Predator-Prey Theory, Ecology, 73 (5): 1530-1535.
- 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
- 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).
- Barro, R. J. & Sala-i-Martin, X. (2004) Economic Growth, Massachusetts Institute of Technology (Chapter 2: p. 85-121).
- Grafton, Q., Kompas, T., Che, N., Chu, L.& Hilborn, R. (2012) BMEY as a Fisheries Management Target, Fish and Fisheries, 13: 303–312.
- 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
- Chu, L., Grafton, R. Q., & Stewardson, M. (2018). Resilience, decision-making, and environmental water releases. Earth's Future, 6, 777–792.
- 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.
- Chu, L., Kompas, T. & Grafton, Q. (2015) Impulse Controls and Uncertainty in Economics, Environmental Modelling and Software, 65: 50-57
- Adda, J. & Cooper, R. (2003) Dynamic Economics Quantitative Methods and Applications, Massachusetts Institute of Technology (Chapter 2: p. 7-24).
Staff FeedbackStudents 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 FeedbackANU 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||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 task||Value||Due Date||Return of assessment||Learning Outcomes|
|Quiz 1||15 %||10/03/2021||10/03/2021||1, 2, 3|
|Quiz 2||15 %||20/04/2021||20/04/2021||1, 2, 3|
|Quiz 3||15 %||20/05/2021||20/05/2021||1, 2, 3|
|Final exam||55 %||10/06/2021||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
PoliciesANU 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 RequirementsThe 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 AssessmentMarks 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
Learning Outcomes: 1, 2, 3
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
Learning Outcomes: 1, 2, 3
Computer-based; Two attempts allowed; Multiple choice format; Students are free to discuss. Covering topics 2 and 3.
Assessment Task 3
Learning Outcomes: 1, 2, 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
Learning Outcomes: 1, 2, 3
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 IntegrityAcademic 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 SubmissionThe 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 SubmissionFor 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 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.
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Extensions and PenaltiesExtensions 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.
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Dr Long Chu
Dr Long Chu