This is the homepage for the QuantEcon scientific and high performance computing workshops to be held at the Central Bank of Chile in September 2022.

Instructors:

• John Stachurski (Australian National University, Co-founder of QuantEcon)
• Pablo Winant (CREST and ESCP Business School, lead developer of dolo

The languages of instruction are Python and Julia. Instruction will draw on, but not be limited to, the

• QuantEcon Python Programming lecture series
• QuantEcon Julia Programming lecture series

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

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.

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

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