https://github.com/quantecon/cbc_workshops
Workshops for the Central Bank of Chile
https://github.com/quantecon/cbc_workshops
Last synced: 11 months ago
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Workshops for the Central Bank of Chile
- Host: GitHub
- URL: https://github.com/quantecon/cbc_workshops
- Owner: QuantEcon
- Created: 2021-12-17T02:59:46.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-11-02T17:58:52.000Z (over 3 years ago)
- Last Synced: 2025-06-29T09:38:48.484Z (12 months ago)
- Language: Jupyter Notebook
- Size: 14.4 MB
- Stars: 14
- Watchers: 6
- Forks: 13
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Central Bank of Chile Scientific Computing Workshops
This is the homepage for the [QuantEcon](https://quantecon.org/) scientific
and high performance computing workshops to be held at the Central Bank of
Chile in September 2022. [](https://mybinder.org/v2/gh/QuantEcon/cbc_workshops/HEAD)
Instructors:
* John Stachurski (Australian National University, Co-founder of QuantEcon)
* Pablo Winant (CREST and ESCP Business School, lead developer of [dolo](https://github.com/EconForge/dolo.py)
The languages of instruction are Python and Julia. Instruction will draw on,
but not be limited to, the
* [QuantEcon Python Programming](https://python-programming.quantecon.org/intro.html) lecture series
* [QuantEcon Julia Programming](https://julia.quantecon.org/intro.html) lecture series
## Format
The schedule is
* September 20th - 23rd: Scientific computing with Python
* September 24th - 25th: Weekend break
* September 26th - 27th: Scientific computing with Julia
The format of each day will be
* 08:30 - 10:30: Lecture
* 10:30 - 11:00: Coffee Break
* 11:00 - 13:00: Practice Sessions
* 13:00 - 14:30: Lunch (at Central Bank offices)
* 14:30 - 16:00: Office hours
## 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, optimization and probability.
## Course 1 Topics
John Stachurski will lead sessions on the following topics
* Python for scientific computing
* NumPy array operations on the CPU
* Introduction to the Numba just-in-time (JIT) compiler
* Application: Markov chains, time series models and distribution dynamics
* Application: Search and optimal stopping
* Application: Asset pricing
* Application: Dynamic programming
* Application: Default cascades in financial networks
* Parallelization on the CPU
* Parallelization on the GPU via CUDA
* Automatic differentiation and GPU computing with JAX
Pablo will lead sessions on the following topics:
* Introduction to deep learning methods
## Julia Topics
Pablo will lead sessions on the following topics:
* Introduction to the Julia language
* Types, multiple dispatch and the Julia JIT compiler
* Structural models in Julia
* Perturbation methods
* Time-iteration variants
* Global solution techniques and occasionally binding constraints models
* "improved" algorithms
* multistep problems and endogenous grids
* dimensionality reduction (\*)
* Heterogeneous agent models (\*)
* Parallel computing in Julia (\*)
* Performance optimization (\*)
\*: time-permitting