• Course: Computational Economics for PhDs
• Teacher: Florian Oswald, [email protected]
• Class Times: Mondays 10:15-12:15
• Class Location: Salle 22, 27 RSG
• Slack: I invited you to our Slack group. Please sign up!

This is a course for PhD students at the Department of Economics at Sciences Po in Computational Economics.

In this course you will learn about some commonly used methods in Computational Economics. These methods are being used in all fields of Economics. The course has a clear focus on applying what you learn. We will cover the theoretical concepts that underlie each topic, but you should expect a fair amount of hands on action required on your behalf. In the words of the great Che-Lin Su:

Doing Computation is the only way to learn Computation. Doing Computation is the only way to learn Computation. Doing Computation is the only way to learn Computation.

True to that motto, there will be homeworks for you to try out what you learned in class. There will also be a term paper. It will be helpful to bring a laptop to the sessions if you have one.

Topics will be demonstrated through live-code examples/slides, available at https://scpo-compecon.github.io/CoursePack/.

1. You need a laptop. No programming skills required.
2. You must sign up for a free account at github.com.
3. Before you come the first class, please do this:
   1. Download the latest stable julia release (v1.0.3 as of today) for your OS.
   2. Start julia by double-clicking on the relevant icon
   3. In the julia console:
      1. type ] (switches into the Package Manager Mode)
      2. type add IJulia InstantiateFromURL and hit Enter
      3. type using InstantiateFromURL and hit Enter
      4. type (or copy/paste) activate_github("QuantEcon/QuantEconLectureAllPackages", tag = "v0.9.5", add_default_environment = true). Hitting Enter will download a bunch of packages and it will compile them for about 10 minutes. Let your computer just run.

1. If you followed Hugo L'Huillier's introduction to programming class last year, you should be all set. If you haven't, or would like a refresher, why not follow introduction to programming taught by Clement Mazet this semester (staring January 2019)? I warmly recommend to attend this course if you did not sit Hugo's course.
2. We will be using Julia for this course.
   • Noteworthy Differences from Other Languages
   • MATLAB, Python, Julia Syntax Comparison
3. Clement in his course will introduce you to things like the Unix Shell and the verion control system Git. Both of those are very useful - for this course, and for the rest of your life as a scientist. If you want to get a headstart, why not have a peek at those excellent tutorials:
   1. Software Carpentry: The Unix Shell: If you have never heard of unix, please go over the first three (very short) chapters:
      • chapters:
        • http://swcarpentry.github.io/shell-novice/01-intro/
        • http://swcarpentry.github.io/shell-novice/02-filedir/
        • http://swcarpentry.github.io/shell-novice/03-create/
      • Where is my Shell Terminal?
        • If you are on Mac OSX, go to Applications, Utilities, Terminal.
        • On Linux, I bet you know.
        • On Windows, you need to make up for the fact that you are not a Unixy system. I recommend Gnu on Windows (GOW)
4. What is Version Control? watch this 5 minute video.

There will be homeworks. They will be listed within the Course Outline.

We will try and further develop a prototype for a new course allocation algorithm for SciencesPo students. There was a task force on this 2 years ago to work on a solution, and they came up with a workable algorithm. Last year's CompEcon class developed a prototype implemetnation. This year we will try to complete what we started with last year!
(:wrench:, :muscle:, :tada:) => (acquire the tools, do the work, tada!)

You will work in teams of 2/3. Details tbc.

There are some excellent references for computational methods out there. This course will use material from

• Fackler and Miranda (2002), Applied Computational Economics and Finance, MIT Press
• Kenneth Judd (1998), Numerical Methods in Economics, MIT Press
• Nocedal, Jorge, and Stephen J. Wright (2006): Numerical Optimization, Springer-Verlag
• Kochenderfer and Wheeler (2019), Algorithms for Optimization, MIT Press

1. Talk through homework requirements
2. Talk through term project requirements
3. Show where material is and do first set of slides.

• Sign up to github.com.
• Sign up for introduction to github and send me a screenshot of all completed issues.
• Make a pull request.

• Setup environment
• Tools and Editors
• Examples
• Types
• Essentials
• Speed
• Data and Statistical Packages

1. Numerical Integration
   • Monte-Carlo integration
   • Gaussian Quadrature
   • Multidimensional Quadrature
     • Quadrature with correlated shocks
2. Function Approximation
   • Polynomial Interpolation
     • Basis functions and Coefficients
   • Regression as Approximation
   • Colocation Methods
   • Multidimensional Approximation
     • The Smolyak Grid

1. Intro
2. Conditions for Optima
3. Derivatives and Gradients
4. Numerical Differentiation
5. JuliaOpt

1. Bracketing
2. Local Descent
3. First/Second Order and Direct Methods
4. Constraints

• Review of DP theory
• Different Solution methods for different cases
  • Discretization
  • Parametric approximation methods basically Function Approximation
  • The Endogenous Grid Method
  • Finite time vs inifinite horizon models

• Solving the Growth Model in 7 Different ways

• What is an MPEC?
• How can we cast constrained problems as MPECs?

Applications:

1. MPEC on John Rust's Bus Engine Replacement
2. The Berry-Levinsohn-Pakes (BLP) paper as constrainted optimization problems

• Brief intro to parallel computing concepts
• Parallel computing with julia
• GPU computing with julia

50% contribution to term project, 50% homeworks

We will try to honour Science Po's anti-plagiarism policy:

Plagiarism occurs when a student submits work that does not allow one to distinguish the student's own thoughts from those of other authors: it can be characterised by the absence of citation of a group of consecutive words (five or more), by reformulation or translation, or by copying directly." (article on intellectual honesty)

Reuse and building upon ideas or code are major parts of modern software development. As an economist writing code, you will (hopefully) never write anything from scratch. This class is structured such that all solutions are public. You are encouraged to learn from the work of your peers. As I said above, I won't hunt down people who are simply copying-and-pasting solutions, because without challenging themselves, they are simply wasting their time and money taking this class.

Please respect the terms of use and/or license of any code you find, and if you reimplement or duplicate an algorithm or code from elsewhere, credit the original source with an inline comment.

I took the setup for the structure of this course from https://github.com/advanced-js taught by Aidan Feldman and team at NYU, and I would like to thankfully acknowledge making the materials useable to other teachers. The same license applies (below). The coursepack material is based on Chris Rackauckas' excellent Julia Intro. The license allows you to copy and use everything here, under the condition that you attribute the work (details in the license). The copyright notice to be included in any such copies and other derivative work is:

Copyright 2019 Florian Oswald, Sciences Po Paris, [email protected]

Thank you.

This work and all other materials under https://github.com/ScPo-CompEcon are licensed under a Creative Commons Attribution 4.0 International License.