sunbin0324 / numericalmethods

website for numerical methods course

Computational Economics for PhDs

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• Teacher: Florian Oswald, [email protected]
• Class Times: Fridays 10:15-12:15 starting 29 Jan 2021
• Class Location: Zoom
• Slack: There will be a slack channel for all communication

Course Description

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

Course Overview

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.

Prerequisites

1. You need a laptop.
2. You should be familiar with the material from Introduction to Programming taught by Clement Mazet in M1. Check out the materials here
3. You must sign up for a free account at github.com. Choose a reasonable user name and upload a profile picture.
4. Before you come the first class, please do this:
   1. Download the latest stable julia release for your OS.
   2. Download the VSCode Editor

Getting Programming Skills

1. Check out Clement Mazet's materials. You must know this level.
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.
4. What is Version Control? watch this 5 minute video. and go back to Clement's stuff if unclear.

Homeworks

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

Term Project

This year your term project will be to replicate a paper published in an economics journal. Ideally this would be related to your field of interest. The requirements for choice of paper to replicate are:

1. Published version and replication kit is available online.
2. The paper to replicate must not use julia.
3. You must use julia for your replication.
   • Ideally your choice will involve at least some level of computational interest (i.e. more than an IV regression)
   • However, you can replicate a paper with an IV regression, but you have to go all the way to get the exact same results as in the paper. I.e. if the author typed the stata command ivreg2 lw s expr tenure rns smsa _I* (iq=med kww age), cluster(year) you will have to write (or find) julia code which will match all output from this, including standard errors. I do not recommend to do this.
4. You need to set up a public github repository where you will build a documentation website of your implementation. You'll learn how to do this in the course.
5. I encourage you to let the world know about your replication effort via social media and/or email to the authors directly. This is independent of whether you were able or not to replicate the results. Replication is not about finding errors in other peoples' work. If you are able to replicate some result in julia, this may be very interesting for others.

Grade

Your grade will be 60% homeworks, 40% term project.

Textbooks

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
• A Gentle Introduction to Effective Computing in Quantitative Research - What Every Research Assistant Should Know, Harry J. Paarsch and Konstantin Golyaev

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License

The copyright notice to be included in any copies and other derivative work of this material is:

Copyright 2021 Florian Oswald, Sciences Po Paris, [email protected]

Thank you.

This is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License