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
- Dynamic factor models and FAVARs
- Time varying parameter models (TVP-VARs)
- Deep learning for time series analysis
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.