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Introduction to Bayesian Analysis: Theory, Applications, and Software Packages
Associate Prof. Grigorios Emvalomatis, University of Crete
Description: The Bayesian approach to data analysis provides an intuitive way to handle stochasticity in econometric models, as well as the ability to specify models tailored to the particularities of each application. In this workshop we will cover the fundamental concepts of Bayesian analysis and, while briefly examining how these concepts apply to the linear model, we will quickly move on to more complex models, such as stochastic volatility and mixed-frequency VAR. The goal is for participants to become familiar with software packages that simplify the specification and estimation of models using the Bayesian approach. We will mainly use the packages JAGS (Just Another Gibbs Sampler) and Stan, which we will run through R. Finally, we will briefly review the capabilities of Python’s PyMC package.
If you would like to run the analysis on your own computer during the workshop, please install the following packages beforehand:
- R and RTools (RTools is required to use Stan)
- Optionally, RStudio or Jupyter Lab
- The R packages rstan, rjags, R2jags and coda
- JAGS
- Python with the packages pymc, arviz, numpy, pandas, and matplotlib
