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šŸ‘‹ Hi there, my name is Dawie van Lill

Here are some things to know about me

  • šŸ”­ Iā€™m currently working on topics related to economic inequality with application to South Africa.
  • šŸ”­ Iā€™m also working on models that integrate financial frictions into a dynamic general equilibrium setting.
  • āš” Fun fact: You pronounce my name Dah-Vee Fun Lill.

Here are some of the things I am currently learning more about

  • Programming. Primarily in Julia, Python and R.
  • Computational methods for dynamic general equilibrium models.
  • Functional analysis and measure theory.
  • Deep learning and scientific machine learning.

You are free to use the code for my courses in any way you wish. I have borrowed from so many people that I feel it is only fitting to make things freely available. Most of the notes are a work in progress. I try to learn as I put notes together, so there might be mistakes.

Dawie van Lill's Projects

doom-emacs icon doom-emacs

An Emacs framework for the stubborn martian hacker

dsbook icon dsbook

Repository for data science book

dsge.jl icon dsge.jl

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

dynamichmc.jl icon dynamichmc.jl

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

dynare.jl icon dynare.jl

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

ec524w20 icon ec524w20

Masters-level applied econometrics courseā€”focusing on predictionā€”at the University of Oregon (EC424/524 during Winter quarter, 2020 Taught by Ed Rubin

econometrics icon econometrics

Econometrics lecture notes with examples using the Julia language

fastai icon fastai

The fastai deep learning library, plus lessons and tutorials

fastbook icon fastbook

The fastai book, published as Jupyter Notebooks

financial-frictions icon financial-frictions

Interactive guide to FernƔndez-Villaverde, Hurtado, and NuƱo (2019): "Financial Frictions and the Wealth Distribution".

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hankpy icon hankpy

Code for solving HANK models in continuous time in Python using numba and UMFPACK

hark icon hark

Heterogenous Agents Resources & toolKit

introtojulia icon introtojulia

A Deep Introduction to Julia for Data Science and Scientific Computing

jprobml icon jprobml

Julia code for Probabilistic Machine Learning

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