• Class Number 2456
  • Term Code 2930
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • AsPr Timothy Kam
    • AsPr Timothy Kam
  • 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

This course introduces contemporary theory for examining central questions in macroeconomics: growth, unemployment, inflation, and business cycles.

Learning Outcomes

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

  1. Be familiar with the main macroeconomic models used to study economic growth and the business cycle
  2. Be able to formulate in general equilibrium simple intertemporal models of household and firm behaviour
  3. Understand the key shocks and propagation mechanisms present in business cycle models
  4. Be able to solve and employ simple stochastic business cycle models to address questions about the macro-economy

Research-Led Teaching

Some of the skillsets, major questions, insights and case studies learned in this course relate di- rectly to the frontier work your instructor and his colleagues are engaged in. In particular, the instructor’s emphasis on physical presence of students in intellectual discourse, self-disciplined learning, critical and research-like independent thinking is designed to encourage students to be- come leaders in their own future spheres who are capable of tackling new and challenging issues. Your instructor is an active researcher in the fields of Macroeconomics and Monetary Economics. He sometimes develop new computational methods for solving difficult economic problems, such as dynamic public insurance games in the face of agent heterogeneity, or in models with endogenous market incompleteness in which monetary policy has a non-trivial redistributive role. He publishes regularly in the leading journals of his fields. He is also a regular visitor and contributor to leading policy institutions around the world, such as the U.S. Federal Reserve Bank system, the Reserve Bank of New Zealand, Bank of Japan, and the Hong Kong Monetary Authority. He currently serves as Treasurer and Chief Technology Officer of the not-for-profit Australasian Macroeconomics Society, and, as the convenor of Australia’s leading 4-th-year Honours in Economics program.

Required Resources

  • Recursive Macroeconomic Theory, 4th Edition, 2018 (MIT Press): : Main textbook*

by Lars Ljungqvist and Thomas J. Sargent (“LS”)

ISBN-13: 978-0262038669

  • Economic Dynamics in Discrete Time, 2014 (MIT Press)

by Jianjun Miao (“Mi”)

ISBN: 978-0262027618

  • Custom Notes (a.k.a. “CN”):
  • Linked from WATTLE
  • Other Useful References:
  • Carl E. Walsh (2003). Monetary Theory and Policy. MIT Press. (“Wa”)
  • Ed Nosal and Guillaume Rocheteau (2011), Money, Payments, and Liquidity.  MIT Press. (“NR”)
  • Daron Acemoglu (2009). Introduction to Modern Economic Growth. MIT Press. (“Ac”)
  • Jerome Adda and Russell Cooper (2003). Dynamic Economics. MIT Press.
  • Mario J. Miranda and Paul L. Fackler (2002). Applied Computational Economics and Finance. MIT Press.
  • Ben J. Heijdra and Frederick van der Ploeg (2002). The Foundations of Modern Macroe- conomics. Oxford University Press.
  • John Stachurski and Thomas J. Sargent (2014-). Quantitative Economics (http://quant-econ. net/)
  • Nancy Stokey and Robert E. Lucas, Jr. (with Ed Prescott). Recursive Methods in Eco- nomic Dynamics. Harvard University Press.
  • Charles I. Jones (2013), Macroeconomics, 3rd International Student Edition, New York: Norton. (For undergraduate revision.)
  • David Romer (2006), Advanced Macroeconomics, 3rd Edition, McGraw-Hill. (For diploma- level revision.)

*Copies are available from the ANU Library’s 2-Hour Reserve listing.


Scientific Computation:

 The modern economics student is expected to possess not just analytical skills but increasingly computational skills, both in academia and in the wider marketplace for economists. You are not expected to have any prior training in such skills, but you are expected to have a flexible and open mind towards learning it as we go.

In this course, we will use the high-level (i.e. user friendly) programming language called Python (currently version 3.7) (http://python.org or https://store.continuum.io/cshop/anaconda/).

Staff Feedback

In-class Activities

  • To maximize your experience and feedback on your progress, please attempt all the tutorial problem sets before attending tutorials.
  • Most of the learning is reinforced through solving problems on your own and being able to discuss it with the class afterwards.
  • An incentive that encourages you to excel in this dimension is provided in the TP and RA assessment items.

Lecturer and Teaching Assistants' Office Hours

  • For maximal value, you should have read the relevant materials (textbook, lecture slides) and attempted problems, before turning up to office hours with questions.
  • If you have any difficulties, please do not hesitate to come and see us; and do not wait until the end of semester to do so.
  • We are here to assist your learning and also to ensure that your university experience continues to be a fun and rewarding one!
  • Note: This course does not encourage last minute rote learning. There will be no additional (i.e., pre-examination) office hours provided beyond Week 12 of the instruction period. You are encourage to seek help early on during the semester.

Tests of Progression and Assignments

  • Answers to these activities and general discussions relating to how you understood the material tested will be provided in class.
  • Your work will be returned to you with comments.


  • Feel free to post short questions related to the course material on WATTLE Forum. The usual internet etiquette applies. The teaching team may answer your questions occasionally. However, please reserve long queries to physical office hours, as we can best help you there.

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 Basic skillset: health check and tool up Tooling Up Week: Some Basics of Scientific Programming in Python. Random Variables and Stochastic Processes; Example of a Stochastic OLG Model; Simulating “cyclical” economic outcomes. Reading assignment: CN; LS, 2 NOTE: Reading references (e.g., "Mi", "LS" and etc.) are referred to in "Required Resources" section below. Legend: TP (Test for Progress, 2 items) RA (Regular Assignments, 4 items)
2 Fundamental ideas of this course: Recursions, economic dynamics and explaining reality Economic Growth and Empirical Regularities. Prior Encounters—Recursive Equilibrium by Example: Solow-Swan and other model variations; OLG model. Special Cases: Linear Dynamical Systems. Reading assignment: Mi 1.1-1.6 and 2.1; LS, 2 and 9 (optional); Ac, Ch-9
3 Mainstream theoretical framework: General equilibrium, finance and macro dynamics (I) Business-cycle Measurement and Empirical Regularities Complete financial markets benchmark: Representative- agent result Asset Pricing Fundamental Welfare Theorems of General Equilibrium: Equivalent Planner Problem Model Variations Reading assignment: CN; Mi, 13-14; Wa, 2, 3 (optional) TP1 RA 1 Available beginning of Week 2 Topics from Weeks 1 and 2 Due on Friday at 5pm of Week 3 (AEST, Wattle Server Time)
5 Dynamic programming and Application 1: RBC models Turning infinite-horizon decision problems (infinite- dimensional optimization) into recursive finite- dimensional problems Application to RBC models Approximate linear solution methods (Undetermined Coefficients Method); Connections to black-box time- series modelling; Taking Theory to Data: Structural Estimation Reading assignment: CN; Mi 12; LS, 2 RA 2 Available beginning of Week 5 Topics from Weeks 3 and 4 Due on Friday at 5pm of Week 6 (AEST, Wattle Server Time)
7 Non-optimal economies, Euler Operators and Application 2: Distorted NK models More Empirical Evidence and Policy Issues - Connecting to mainstream macro data and thinking Rationalizing the undergraduate AD-AS framework as New Keynesian model Solving and Simulating a NK economy; Policy Counter-factuals Introduction: Dynamically inconsistent policy plans and credible public policies Reading assignment: CN; Mi 19, 21; Wa, 8 RA 3 Available beginning of Week 7 Topics from Weeks 5 and 6 Due on Friday at 5pm of Week 8 (AEST, Wattle Server Time)
8 Dynamic programming (again) and Application 3-4: Job Search and Non-Walrasian Matching Markets Connecting to undergraduate "bathtub model" and long run data on labor markets Unemployment, Job Search and Matching Money, Finance and Payments Revisited: A Critique of Mainstream Monetary Policy Models Reading assignment: Mi, 18; LS, 6; NR, 1-4 TP2
10 Application 5: Incomplete asset markets, agent heterogeneity and wealth inequality Extending lessons from Week 2 and 4: When does the representative agents result break apart? How to think about the rich vs poor? Whither model? Connecting back to micro data on wealth and consumption inequality RA 4 Available beginning of Week 9 Topics from Weeks 7, 8 and 9 Due on Friday at 5pm of Week 10 (AEST, Wattle Server Time)
11 Application 6: Incomplete international asset markets and sovereign debt defaults How to rationalize observed failures of sovereign governments in repaying international debt? How do we think about excess volatilities in emerging country business cycles?
12 Application 7: Woodshed sessions Where have we been and where to from here? Q & A Problem solving bootcamp

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Test of Progress (TP) 10 % 25/02/2019 31/05/2019 1,2,3,4,5
Regular Assignments (RA) 30 % 25/02/2019 31/05/2019 1,2,3,4,5
Final Examination (FE) 60 % 06/06/2019 04/07/2019 1,2,3,4,5

* 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.


(FE) Final Examination (60% to 100%). Completion of the final examination is necessary for a successful completion of the course. If you do not complete the final examination you will fail the course. Feedback will not be provided on the final examination, by the very definition of a final examination.

Assessment Task 1

Value: 10 %
Due Date: 25/02/2019
Return of Assessment: 31/05/2019
Learning Outcomes: 1,2,3,4,5

Test of Progress (TP)

Value: 0% - 10%

There will be two in-class tests (in the format of online WATTLE quizzes or alternative paper version of the quizzes) to test you on your learning progress. This is an optional and redeemable component, so you may choose to not participate, for whatever reason. Your choice to opt out of this assessment will automatically shift the weight of this component toward the final examination.

Your overall course mark will be calculated according to this formula:

 max { (0.6 × FE + 0.1 × TP + 0.3 × RA) , (0.9 × FE + 0.1 × TP) , (0.7 × FE + 0.3 × RA) , FE } ,

where FE, TP and HW refer to the Final Examination, Test of Progress and Regular Assignments, respectively.

These components are graded out of 100%. The nature of these assessments and their requirements are further defined below.

Assessment Task 2

Value: 30 %
Due Date: 25/02/2019
Return of Assessment: 31/05/2019
Learning Outcomes: 1,2,3,4,5

Regular Assignments (RA)

Value: 0% - 20%

This is an optional and redeemable component, so you may choose to not participate, for whatever reason. Your choice to opt out of this assessment will automatically shift the weight of this component toward the final examination.

Your overall course mark will be calculated according to this formula:

max { (0.6 × FE + 0.1 × TP + 0.3 × RA) , (0.9 × FE + 0.1 × TP) , (0.7 × FE + 0.3 × RA) , FE } ,

where FE, TP and HW refer to the Final Examination, Test of Progress, and Regular Assignments, respectively.

These components are graded out of 100%. The nature of these assessments and their requirements are further defined below.

Assessment Rubric:

Each Regular Assignment (RA) submission will be graded based on indicative evidence of preparation defined by the discrete levels of 30% (poor), 70% (adequate) or 100% (exceptional). The evidence sought must contain:

  • (90%) Indications of technical competency and understanding (e.g., in terms of logical thinking, clarity of solutions and code) and overall ability to communicate with the reader and to explain the subject matter and analysis. Equal weight will generally be assigned to both considerations.
  • (10%) Proper citations of references and other sources of information used, and where relevant, replicability of human/machine computed results.
  • Where indicated, assignments must be submitted as working (replicable) Jupyter Notebook files.
  • Students are expected to be able to figure out how to write mathematics in the LaTeX typesetting format and to learn to use Jupyter Notebooks. No other exceptions will be allowed.


Feedback will be provided (in class) on assessments either on the board or via paper handouts. Students, at this level of maturity, are expected to be able to think about the feedback and reconcile them with their own attempts. Students are encouraged to discuss any questions they have regarding their assessment feedback with the course teaching assistants.

You are expected to work through the RA assignments on your own without any instructor input. As a matter of fairness to all students, please do not ask the instructor for help when attempting RA tasks.

Assessment Task 3

Value: 60 %
Due Date: 06/06/2019
Return of Assessment: 04/07/2019
Learning Outcomes: 1,2,3,4,5

Final Examination (FE)

Value: 60% - 100%

Completion of the final examination is necessary for a successful completion of the course. If you do not complete the final examination you will fail the course.

Your overall course mark will be calculated according to this formula:

max { (0.6 × FE + 0.1 × TP + 0.3 × RA) , (0.9 × FE + 0.1 × TP) , (0.7 × FE + 0.3 × RA) , FE }

where FE, TP and HW refer to the Final Examination, Test of Progress, and Regular Assignments, respectively.

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. You do not need to submit via Turnitin.

Regular Assignments (RA) must to be submitted via WATTLE as Jupyter Notebooks with replicable content (unless stated otherwise on RA problem sheet).

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 assignments will not be graded. If an assessment task is not submitted by the due date, a mark of 0 will be automatically awarded.

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 Timothy Kam
6125 1072

Research Interests

Macroeconomic Theory and Policy, Monetary Economics, Computational Economics

AsPr Timothy Kam

Monday 11:00 12:00
Monday 11:00 12:00
AsPr Timothy Kam
6125 1072

Research Interests

AsPr Timothy Kam

Monday 11:00 12:00
Monday 11:00 12:00

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