These course notes are for the section on Bayesian econometrics. Incomplete, but frequently updated.
The notebooks/dynamic/
folder contains all the interactive Pluto
notebooks for this class. To get started with Pluto, please visit the Github page or watch this video
- [Self Study] Introduction to Julia from José Eduardo Storopoli
- Sampling, random variables and distributions -- Lecture #1 Notebook
- Bayesian thinking (Bernoulli / Binomial) -- Lecture #2 Notebook
- Starting with simulation (Normal) -- Lecture #3 Notebook
- Markov chain Monte Carlo -- Lecture #4 Notebook
- Bayesian linear regression -- Lecture #5 Notebook
- Bayesian Vector Autoregression (BVARs) -- Lecture #6 Notebook
- State space models / Kalman filter -- State space models and Kalman filter
R
code for many of the lectures will also be uploaded, for those that are more comfortable using R
. However, the main programming language for this course will be Julia
. No familiarity with Julia
is assumed. We will be starting from basic principles.
Below is a non-exhaustive list of the resources used to construct the notes for this course. I owe a debt of gratitude to these wonderful people for making resources freely available.
- MIT (2021). Computational Thinking. -- NB resource! Most of the first lecture based on this.
- QuantEcon (2021). Quantitative Economics with Julia. -- Lectures 4, 7
- Aki Vehtari (2020). Bayesian Data Analysis. -- Lectures 2, 3, 4
- José Eduardo Storopoli (2021). Bayesian Statistics with Julia and Turing. -- Lectures 2, 3, 4
- Gary Koop (2021). Bayesian Econometrics. -- Lectures 5, 6
- Joshua Chan (2017). Notes on Bayesian Econometrics. -- Lectures 5, 6
- Jamie Cross (2020). Introduction to Bayesian Econometrics -- Lectures 2, 3, 4, 5, 6
We will also make use of notes from the University of Queensland.