ECON 408: Computational and Quantitative Macroeconomics

This is a newly proposed course which will be taught as ECON 407 in Spring 2023 as a pilot.

See below for additional strongly recommended prerequisites. To summarize: beyond the intermediate micro you are expected to have a ECON 323 or a similar programming course using Python, Matlab, or similar languages (i.e., Stata and R are insufficient). Finally, you should have MATH 221 or an equivalent course in matrix algebra—which would already fulfilled by those taking ECON 323.

Calendar Description:

ECON 408: Computational Methods in Macroeconomics

(3 credits course) Computational tools used in macroeconomics and financial economics including applications to unemployment, inequality, asset pricing, and economic growth

Prerequisites: One of ECON 301, ECON 304, ECON 308 and one of ECON 323, CPSC 103, CPSC 107, CPSC 110, MATH 210, COMM 337 and MATH 221

Course Overview

This is a course in computational tools used in macroeconomics. You are expected to have some proficiency in Matlab, Python, Julia, and similar languages as fulfilled in the prerequisites (i.e., ECON 323, CPSC 103, CPSC 107, CPSC 110, MATH 210, COMM 337). You are not required to have taken intermediate macroeconomics in order to enrol.

Models in macroeconomics and financial economics are constructed from a core set of tools which model each agents’ decisions while maintaining an internal consistency between the decisions of complicated distributions of other agents in an economy. Some of the common features of these models include:

Some of the common features of models in macroeconomics and financial economics include:

  1. dynamic and forward looking decisions: If I consume less today, I can save more for tomorrow;
  2. randomness and uncertainty about the future: If reject a job offer, I am not sure when the next offer will occur;
  3. prices and resources reflecting the collective decisions of other agents: The wage I am offered depends on the number of similar workers I am competing with, the intensity which the unemployed search for jobs, and the demand for my skills from firms;
  4. social learning from other agents’ with information aggregated through prices: If many others consider a particular equity or bond asset a good buy, then I can infer this by the price of the asset itself; and
  5. distributions and heterogeneity in the economy influencing decisions and prices: If the distribution of income is askew and there are many poor agents living hand-to-mouth, government policy such as sending out stimulus cheques has a different effect on inflation and consumer welfare than if every person had similar incomes.

This course is designed to jointly explore these sorts of theoretical models in conjunction with the computational tools to solve and simulate them. We will learn using the Julia programming language—–a modern language for scientific and technical computing. If you have an existing background in Python or Matlab from other courses you will find that Julia compliments those skills.

Learning Outcomes

By the end of the course, you will be able to

  1. program using tools from linear algebra, probability, and optimization in the Julia programming language (LO1)
  2. simulate and analyze stochastic processes for the purpose of understanding the evolution of the wealth distribution (LO2)
  3. describe economic dynamics as a linear state space model and solve them numerically (LO3)
  4. implement and analyze Markov chains, and apply them to models of unemployment and asset pricing (LO4)
  5. investigate the role of general equilibrium and prices in aggregating information and reflecting the real economy (LO5)
  6. define economic problems recursively, such labor market search and consumption savings models, and solve them numerically (LO6)
  7. define and implement dynamic models of growth (LO7)

Textbook and Materials

The core textbook is the online, open-source textbook Quantitative Economics with Julia by Jesse Perla, Thomas J. Sargent and John Stachurski which contains both graduate- and undergraduate-level material. In cases where material in the course is too advanced, we will choose a subset and adapt lecture materials to be appropriate for the level.

The textbook includes both theory and code, and a set of Jupyter notebooks.

All materials will be provided on http://canvas.ubc.ca and the open-source materials are on GitHub.

Course Format

The course will meet for two 1.5 hours lectures per week for an in-class lecture. While there will not be a formal “lab”, the instructor may go through coding examples in class.

After the first few weeks the lectures will tend to focus on teaching the theory where code implementations done largely in the assignments. At that point, much of the coding practice will be done in the problem sets, leaving most class-time for connecting the macroeconomic models to the methods.

Assignments and Assessment

The only way to learn how to apply programming to economic problems is practice. To aid this, a significant portion of the assessment will be in the form of problem sets.

The midterm and final will be in class and combine both theory and computations, with the exam submitted via a Jupyter notebook.

The weighting in the grade is:

  • Six Problem sets: 30% (total)
  • Midterm exam: 30%
  • Final exam: 40%

Computational Infrastructure and Programming Language

While you will have experience with another programming language such as Python or Matlab from your prerequisites, this course will be taught using Julia. Beyond being an excellent language for technical computing and popular among macroeconomists, Julia provides a new set of programming principles that will broaden the your knowledge of computing. This will help you by both providing a better differentiated resume, broader skills, and more opportunities to work as a research assistant for researchers requiring significant computational expertise.

You can install Julia on your laptop by following these instructions. While one can use Julia entirely from just Jupyter notebook, we will also introduce basic GitHub and VS Code usage as well to help broaden your exposure to computational tools.

Course Outline (by Week)

Week 1: Julia and programming for economics (LO1)

  • Topics include: Getting Started and Julia Essentials
  • At the end of the week you will have reviewed the basic setup of the Julia programming language and can comfortably accomplish simple tasks as they would in to Python or Matlab.

Week 2: Linear algebra and basic scientific computing (LO1)

  • Topics include Arrays and Related Types and related topics in implementing Linear Algebra. In addition, you will review Optimizers and Solvers.
  • At the end of the week you will feel comfortable working with matrices, vectors, and arrays; solving linear systems and calculating eigenvalues; optimizing unconstrained and constrained functions; and solving systems of equations.

Week 3: Geometric Series and Stochastic Processes (LO2)

  • Topics include Geometric Series and Dynamics in One Dimension
  • At the end of the week you will understand how to calculate present discounted values, work with Keynesian money-multipliers, and simulating random processes.
  • Problem Set 1 Due - basic loops, linear algebra, and optimization problems.

Week 4: Dynamics of Wealth and Distributions (LO2)

  • Topics include AR1 Processes and Wealth Distribution Dynamics
  • At the end of the week you will better understand ergodic distributions, measures of inequality, and how to simulate the dynamics of the wealth distribution.

Week 5: Linear State Space Models Part (LO3)

  • Topics include Linear State Space Models
  • At the end of this week you will understand how to describe processes such as asset pricing and consumption smoothing as linear-state space models, simulate them, and calculate present-discounted values using those stochastic processes.
  • Problem Set 2 Due - calculating present discounted values and simulating univariate asset pricing models, simulating and calculating dynamics of the wealth distribution.

Week 6: Permanent Income Model (LO3)

  • Topics include The Permanent Income Model
  • At the end of the week you will understand how to implement the classic consumption-savings model with linear-quadratic preferences in the LSS framework of the previous lecture, and to simulate permanent and transitory shocks to income.

Week 7: Markov Chains (LO4)

  • Topics include Finite Markov Chains
  • At the end of the week you will understand how to describe discrete-state stochastic processes as Markov chains and simulate models of unemployment for a worker.
  • Problem Set 3 Due - solving and simulating multivariate asset pricing problems in a LSS setup and exploring the permanent income model.

Week 8: Models of Unemployment (LO4)

  • Topics include the Lake Model of Employment and Unemployment
  • At the end of the week you will build on the previous tools of Markov chains to look at a aggregated models of employment and unemployment in the economy.
  • Midterm in class

Week 9: Rational Expectations and Firm Equilibria (LO5)

  • Topics include Rational Expectations Equilibrium
  • At the end of the week you will understand the core “big K, little k” insight for implementing rational expectations equilibria and apply it to models of firm dynamics.

Week 10: Asset Pricing (LO5)

  • Topics include Asset Pricing with Finite State Models
  • At the end of the week you will understand pricing assets with payouts following a Markov-chain as derived in the previous lectures.
  • Problem Set 4 Due - firm dynamic simulations and more on dynamics of Markov chains.

Week 11: Lucas Trees (LO5)

Week 12: Recursive Equilibria and the McCall Search Model (LO6)

  • Topics include The McCall Search Model
  • At the end of the week you will be able to define and solve basic models of labor market search.
  • Problem Set 5 Due - asset pricing examples and a labor market search.

Week 13: Cake Eating Problem (LO6)

Week 14: Cass Koopmans/Neoclassical Growth (LO7)

  • Topics include Cass Koopmans Planning Problem
  • At the end of the week you will be able to solve for the transition dynamics of the planning problem for the Cass Koopmans/neoclassical growth model.

Week 15: Optimal Growth Model (LO7)

  • Topics include Stochastic Optimal Growth Model
  • At the end of the week you will be able to solve growth models with a single type of good and stochastic productivity.
  • Problem Set 6 Due - solving stochastic dynamic programming and simulating transition dynamics of growth models.
  • Final Exam - according to calendar schedule.

Policies

Missed Exam Policy: You are responsible for ensuring that you take these exams as scheduled; no make-up exams will be given.

  • Missing a midterm for ANY acceptable reason will result in its weight being automatically transferred to the final exam.
  • The final exam date will be announced by Student Services about half-way through the term.
  • There is no make-up final. Travel plans and/or cheap tickets are not a reason to miss the final. If you have a medical or other compelling reason why you cannot take the final exam at its scheduled time you must follow the formal process and get a Standing Deferred Academic Concession from your Faculty Advising Office (see below)

Policy for Academic Concessions: Sometimes, things happen during the course of a semester that can affect your ability to succeed. There are three main categories:

  • Medical – i.e. you got sick and missed class or a chronic illness got worse
  • Compassionate – i.e. a friend or close relative had something bad happen to them, or something bad happened to you.
  • Conflicting Responsibilities – i.e. something happened in your personal life which is affecting your ability to do the work, like childcare falling through

You can read more about specific examples and the whole policy at: http://www.calendar.ubc.ca/vancouver/index.cfm?tree=3,48,0,0

In all of these cases, UBC’s policy is to allow you to request an academic concession. My policy is that all requests for academic concession on exams should be handled through your faculty Advising office (unless your office advises otherwise). This is so that we can centrally track requests for concession and ensure they are fairly administered; it also helps protect your privacy. You can find the procedure here, for Arts:

https://students.arts.ubc.ca/advising/academic-performance/help-academic-concession/

If you need a concession, you should immediately speak to Advising, who will follow-up with me to handle the academic side of things. In-term concessions, which handle things like missed assignments or deadlines, are handled usually by extending the deadline or adjusting the final grading of the course (e.g. omitting an assessment). Alternative forms of assessment may also be used if suitable and recommended by Advising.

Concessions need to be made in a timely fashion, which I will define as “within 2 weeks of the missed assessment” unless this is not reasonable. You are also welcome to speak to me regarding your issue; I’m here to support you and help you get through things and be successful. If you’re not sure if it’s something you should/could get a concession for, I can also give you a quick sense of what Advising will likely suggest if you’re unable to make an appointment immediately.

Academic Integrity It is the policy of the VSE to report all violations of UBC’s standards for academic honesty to the office of the Dean of Arts. A detailed description of academic integrity, including the University’s policies and procedures, may be found in the UBC Calendar: Student Conduct and Discipline. In addition to the violations stated on that page (for example, plagiarism), the VSE rules state that any student who hires a tutor/editor to help with any portion of their work will be given an automatic grade of zero on their submitted work. Any student found to have violated the university rules on academic misconduct will receive a grade of zero on the relevant work. Further penalties, may be levied by the President’s Advisory Committee on Student Discipline. Those further penalties could include a notation on your transcript indicating that you have committed an academic offence, failure of the course, and/or suspension from the university.

Academic Accommodation for Students with Disabilities The University of British Columbia recognizes its moral and legal duty to provide academic accommodation. The University must remove barriers and provide opportunities to students with a disability, enabling them to access university services, programs, and facilities and to be welcomed as participating members of the University community. The University’s goal is to ensure fair and consistent treatment of all students, including students with a disability, in accordance with their distinct needs and in a manner consistent with academic principles. The University will provide academic accommodation to students with disabilities in accordance with the British Columbia Human Rights Code, R.S.B.C. 1996, c. 210 and the Canadian Charter of Rights and Freedoms, Part I of the Constitution Act, 1982, being Schedule B to the Canada Act 1982 (U.K.), 1982, c. 11. Provision of academic accommodation shall not lower the academic standards of the University. Academic accommodation shall not remove the need for evaluation and the need to meet essential learning outcomes. Students with a disability who wish to have an academic accommodation should contact Centre for Accessibility without delay (see UBC Policy 73).

Conflicting Responsibilities UBC recognizes that students may occasionally have conflicting responsibilities that affect their ability to attend class or examinations. These may include: representing the University, the province or the country in a competition or performance; serving in the Canadian military; or observing a religious rite. They may also include a change in a student’s situation that unexpectedly requires that student to work or take responsibility for the care of a family member, if these were not pre-existing situations at the start of term.

Students with conflicting responsibilities have a duty to arrange their course schedules to avoid, as much as possible, any conflicts with course requirements. As soon as conflicting responsibilities arise, students must notify either their instructor(s) or their Faculty Advising Office (e.g. Arts Academic Advising), and can request academic concession. Instructors may not be able to comply with all such requests if the academic standards and integrity of the course or program would be compromised.

Varsity student-athletes should discuss any anticipated and unavoidable regular-season absences with the instructor at the start of term and provide notice of playoff or championship absences in writing as soon as dates are confirmed. Religious observance may preclude attending classes or examinations at certain times. In accordance with the UBC Policy on Religious Holidays, students who wish to be accommodated for religious reasons must notify their instructors in writing at least two weeks in advance. Instructors provide opportunity for such students to make up work or examinations missed without penalty.

Policies and Resources to Support Student Success UBC provides resources to support student learning and to maintain healthy lifestyles but recognizes that sometimes crises arise and so there are additional resources to access including those for survivors of sexual violence. UBC values respect for the person and ideas of all members of the academic community. Harassment and discrimination are not tolerated nor is suppression of academic freedom. UBC provides appropriate accommodation for students with disabilities and for religious and cultural observances. UBC values academic honesty and students are expected to acknowledge the ideas generated by others and to uphold the highest academic standards in all their actions. Details of the policies and how to access support are available here https://senate.ubc.ca/policies-resources-support-student-success.