• Class Number 4676
  • Term Code 2930
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
  • COURSE CONVENER
    • AsPr Timothy Kam
  • LECTURER
    • 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 will acquaint students with contemporary modern macroeconomics. Key questions relating to long-terms prospects for the wealth of nations and the short-terms fluctuations in aggregate economic outcomes will be discussed. In addressing these questions, we will need to develop some analytical tools, learn about the modern approaches to macroeconomic modelling, and appreciate the importance of empirical regularities in informing modelling. We will also discuss the relevance of some of these models toward informing macroeconomic policy and business decision making. Students are expected to possess or have the aptitude for some formal mathematical thinking and analysis (at a minimal level of ECON8013 Mathematical Techniques in Economics I). 

Learning Outcomes

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

  1. Understand key issues and questions in macroeconomics.
  2. Develop some idea about how to think about and solve current macroeconomic problems.
  3. Understand the connection between assumptions made and the conclusions drawn.
  4. Appreciate the shortcomings of models and to provide alternative improvements.
  5. Construct logical arguments and provide economic explanations consistent with the workings of the model used.
  6. Use analytical and (some) numerical methods in modeling.
  7. Work independently, in teams, and to develop intellectual leadership.

Research-Led Teaching

Some of the skill sets, major questions, insights and case studies learned in this course relate directly 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 become 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.

Examination Material or equipment


Required Resources

•   Carlin, W. and Soskice, D. (2015). Macroeconomics: Institutions, Instability and the Financial System. Oxford University Press.

•   Williamson, S. (2011). Macroeconomics. Addison-Wesley Publishers.

•   Romer, P. (2010). Advanced Macroeconomics. McGraw-Hill Publishers.

•   de la Croix, D. and Michel, P. (2002). A Theory of Economic Growth: Dynamics and Policy in Overlapping Generations. Cambridge University Press.

•   Champ, B. and Freeman, S. (2009). Modeling Monetary Economies. Cambridge University Press.

•   Jones, C. I. (2014). Macroeconomics. Norton.

•   Auerbach, A. J. and Kotlikoff, L. J. (1998). Macroeconomics: An Integrated Approach. MIT Press.

•   Class notes and slides (see link from WATTLE )


There is no need to acquire these books unless you have money to spare. Copies of some of these books are available from the ANU Chifley Library closed reserve system. (Take a walk there. Also, books will have online versions which you can locate via the ANU Library catalog.)

Staff Feedback

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

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.

Lecturer 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. I am here to assist your learning and also to ensure that your university experience continues to be a fun and rewarding one!


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.

Other Information

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. These resources are available through the student computer labs and you can also install it for free via the Anaconda distribution: https://www.anaconda.com/download/. Install the version that says Python 3.7.


Assessment Requirements

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

max {(0.6 × FE + 0.4 × RA) , (1.0 × FE ) } ,where FE and RA refer to the Final Examination and Regular Assignments, respectively. These components are graded out of 100%. The nature of these assessments and their requirements are further defined below. The instructor has the discretion in awarding bonus points for assessable work that is of exceptional quality.


Wattle Forum

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.

Group Study, Self Discipline and taking ownership of learning

–   Group study is encouraged to help reinforce your learning of the material: What better way to check if you have mastered the material than to be able to explain your understanding to a fellow group member? Also, group work helps build your general and economic communication skills, skills that are commonly required in team-based projects in the professional setting.


Scaling

Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.


Support for Students

The University offers a number of support services for students. Information on these is available online    from    http://students.anu.edu.au/studentlife/


Referencing Requirements

References cited should be listed as part of a bibliography at the end of your work. If software code or web resources are used, relevant URL links should also be included.

Your working code should also be included as part of your submission. In this course, you should submit working code along with explanations to evidence your understanding of the work in the form of a Jupyter notebook. We will demonstrate how to create and use a Jupyter notebook early on in the tutorials.

Students may use any accepted bibliographic style. For a professional look and ease of writing using scientific notation, students are encouraged to use LATEX (with BibTEX referencing). The ANU Library offers classes on how to use these tools, or, you can pick this up online.



Class Schedule

Week/Session Summary of Activities Assessment
1 Data and casual theorizing (refresher) • Measuring business cycles • Understanding observational data using Python/Pandas • A common Keynesian policy framework; the MPC • Fiscal and Monetary Policy Reading Assignment: Carlin-Soskice, Ch.1-2; Class Notes; Jones (optional Assignment 1 (out W1)
2 Data and casual theorizing (refresher) • Measuring business cycles • Understanding observational data using Python/Pandas • A common Keynesian policy framework; the MPC • Fiscal and Monetary Policy Reading Assignment: Carlin-Soskice, Ch.1-2; Class Notes; Jones (optional)
3 A Keynesian paradigm: more structure • A dynamic version of the Keynesian macroe- conometric/policy model • Putting into action: simulation and counterfac- tual policies using Python Reading Assignment: Carlin-Soskice, Ch.3 (and appendix); Class Notes; Jones (optional graphical re- fresher) Assignment 1 (due W3) Assignment 2 (out W3)
4 A Keynesian paradigm: more structure • A dynamic version of the Keynesian macroe- conometric/policy model • Putting into action: simulation and counterfac- tual policies using Python Reading Assignment: Carlin-Soskice, Ch.3 (and appendix); Class Notes; Jones (optional graphical re- fresher)
5 Beliefs and policy in a Keynesian paradigm • Case Study: 1970’s stagflation • What if people are not so na¨ive? • Rational Expectations vs. Learning • Putting into action: Designing “optimal” poli- cies • Case Study: Zero nominal interest and deflation Reading Assignment: Carlin-Soskice, Ch.4 Assignment 2 (due W5)
6 Capital, growth dynamics and long run wealth • Measuring and understanding growth facts • Neoclassical growth (Solow-Swan model) and the MPC (again) • Case Study: Institutions and growth • Putting into action: Simulating growth transi- tional dynamics and long run distributions using Python Reading Assignment: Romer, Ch.1
7 A deeper interpretation of the MPC • A Diamond-Samuelson OLG example • Endogenizing the MPC in Solow-Swan; Beyond Adam Smith to Incomplete Markets • Putting into action: Transition dynamics using Python Reading Assignment: Romer Ch.2; Class notes Assignment 3 (out W7)
8 Fiscal policy, heterogeneity and redistribution • How to overcome market incompleteness? • Social security systems from an OLG perspective • What is an efficient policy system? Reading Assignment: Romer Ch.2; Class Notes; De La Croix and Michel, Ch.3 Assignment 3 (due W8)
9 Why Money? • An OLG perspective • Money as a bubble asset; overcoming market in- completeness • Inflation and monetary equilibria • Case Study: Rogue states and Hyperinflation Reading Assignment: Champ, Freeman and Haslag, Ch.1-2 Assignment 4 (out W9)
10 Monetary policy and expectations revisited • An OLG perspective • The famous Lucas critique and the perils of macroeconometric policy modelling • Inflation and monetary equilibria • Case Study: Where Art Thou, O Phillips Curve? Reading Assignment: Champ, Freeman and Haslag, Ch.3
11 Modelling decisions and business cycles • A toy OLG prototype • Putting into action: Solving and simulating stochastic and dynamic equilibrium Reading Assignment: Class notes Assignment 4 (due W11)
12 Woodshed Sessions • Course review and Looking ahead Problem-solving workshop
13 Examination Period Final Examination

Tutorial Registration

 Answers to these activities and general discussions relating to how you understood the material tested will be provided in class.


Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Regular Assignments 40 % 25/02/2019 06/06/2019 1-5
Final Examination 60 % 06/06/2019 04/07/2019 1-5

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.

Participation

Answers to these activities and general discussions relating to how you understood the material tested will be provided in class.

Examination(s)

see Assessment Task 2

Assessment Task 1

Value: 40 %
Due Date: 25/02/2019
Return of Assessment: 06/06/2019
Learning Outcomes: 1-5

Regular Assignments

(RA) Regular Assignments (40%).


The overall 40% of RA is optional and redeemable, so you may choose to not submit any work here, for whatever reason. Each of the four RA components is worth 10% of the course mark.


Important: If you submit only a subset of all 4 RA components (e.g., if you submit only 1 out of the total 4 RA's, then you only earn up to 10% of the course mark, and, your final examination is still worth 60%). You will get 0% for each RA component not submitted for marking. Your choice to opt out of this assessment will automatically shift the weight of 40% toward the final examination.


Each assignment will be graded based on:

•   (90%) Objective evidence 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.


Assignments must to be submitted via WATTLE in PDF format or as Jupyter notebooks with replicable content.


If an assessment task (or its component item, e.g., a particular assignment) is not submitted by the WATTLE -announced due date, a mark of 0 will be awarded. This course does not entertain requests for extension on redeemable assessment items.


Each student must submit an original work and declare it to be so. You will be graded individually.

Assessment Task 2

Value: 60 %
Due Date: 06/06/2019
Return of Assessment: 04/07/2019
Learning Outcomes: 1-5

Final Examination

(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.

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. Unless an exemption has been approved by the Associate Dean (Education) as submission must be through Turnitin.

Assignments must to be submitted via WATTLE in PDF format or as Jupyter notebooks with replicable content.

If an assessment task is not submitted by the WATTLE -announced due date, a mark of 0 will be awarded. This course does not entertain requests for extension on redeemable assessment items.


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

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.

OR

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.

Returning Assignments

Marked assignments will be returned to you, but please keep a copy of your submitted work as a safety precaution. Written feedback will be given on your individually marked assignment.

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
timothy.kam@anu.edu.au

Research Interests


Macroeconomic Theory and Policy, Monetary Economics,

Computational Economics

AsPr Timothy Kam

Monday 12:00 13:00
Monday 12:00 13:00
AsPr Timothy Kam
6125 1072
timothy.kam@anu.edu.au

Research Interests


AsPr Timothy Kam

Monday 12:00 13:00
Monday 12:00 13:00

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