dawievlill Goto Github PK

followers: 123.0 following: 307.0 repos: 112.0 gists: 0.0

Name: Dawie van Lill

Type: User

Company: Stellenbosch University

Bio: Economics lecturer at Stellenbosch University in South Africa. I am interested in computational macroeconomics, Bayesian methods and deep learning.

Location: Stellenbosch, South Africa

Blog: https://dawievanlill.netlify.app/

👋 Hi there, my name is Dawie van Lill

Here are some things to know about me

🔭 I’m currently working on topics related to economic inequality with application to South Africa.
🔭 I’m also working on models that integrate financial frictions into a dynamic general equilibrium setting.
⚡ 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

18337

18.337 - Parallel Computing and Scientific Machine Learning

30-seconds-of-python

Short Python code snippets for all your development needs

addaandcooper

Code to solve exercises from Adda and Cooper's "Dynamic Economics" book

adv-r

Advanced R: a book

advanced-time-series

advancedhmc.jl

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

advbayeslearncourse

Course material for the PhD course in Advanced Bayesian Learning

an-introduction-to-statistical-learning

This repository contains the exercises and its solution contained in the book An Introduction to Statistical Learning in python.

bda_course_aalto

Bayesian Data Analysis course at Aalto

bmr

Bayesian Macroeconometrics in R

bvar

Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015). Allows for the computation of impulse responses and forecasts and provides functionality for assessing results.