https://github.com/quantecon/cbc_2024
https://github.com/quantecon/cbc_2024
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/quantecon/cbc_2024
- Owner: QuantEcon
- License: cc0-1.0
- Created: 2024-04-04T02:05:17.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-17T12:15:01.000Z (about 2 years ago)
- Last Synced: 2025-04-21T03:29:21.580Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 14.1 MB
- Stars: 6
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Central Bank of Chile 2024 Scientific Computing Workshop
This is the homepage for the [QuantEcon](https://quantecon.org/) scientific
and high performance computing workshop to be held at the Central Bank of
Chile in May 2024.
## Instructor
* [John Stachurski](https://johnstachurski.net/) (Australian National University)
Bio: John Stachurski is a mathematical and computational economist who works on
algorithms at the intersection of dynamic programming, Markov dynamics,
economics, and finance. His work is published in journals such as the Journal
of Finance, the Journal of Economic Theory, Automatica, Econometrica, and
Operations Research. In 2016 he co-founded QuantEcon with Thomas J. Sargent.
## Abstract
Open source scientific computing environments built around the Python
programming language have expanded rapidly in recent years. They now form the
dominant paradigm in artificial intelligence and many fields within the natural
sciences. Economists can greatly enhance their modeling and data processing
capabilities by exploiting Python's scientific ecosystem. This course will
cover the foundations of Python programming and Python scientific libraries, as
well as showing how they can be used in economic applications for rapid
development and high performance computing.
## Topics
### Monday: overview and Python intro
* An overview of modern scientific computing
* AI and its impact on economic modeling
* Quick introduction to Python
### Tuesday: scientific Python
* Linear regression with Python
* Accelerating Python using Numba and Fortran
* Inventory dynamics
* Gini coefficients and Lorenz curves
* Wealth dynamics (simple model)
* Markov chains
### Wednesday: JAX, GPUs and autodiff
* Introduction to JAX and GPU computing
* Automatic differentiation
* Autodiff application: Epstein-Zin preferences
* Wealth dynamics revisited
* Inventory dynamics revisited
* Job search
### Thursday: dynamic programming with JAX
* Dynamic programming: theory and algorithms
* Optimal savings problems (JAX)
* Endogenous grid method (JAX)
### Friday: equilibrium models with JAX
* Aiyagari model
* Arellano sovereign default model
* Bianchi overborrowing model
* Hopenhayn industry model
## Dates
* May 13th - 17th
## Prerequisites
All participants should bring laptop computers. If possible, participants
should bring laptops with the ability to install open source software. For those
without such permissions, a cloud computing option will be provided. The courses
assume knowledge of the fundamentals of linear algebra, analysis, dynamic optimization
and probability.
Suitable background can be found in the first few chapters of [Dynamic Programming](https://dp.quantecon.org).