Code/Teaching Notes

I know there are many great packages for MCMC, but I believe that for beginners, simple code really helps build the conceptual framework. Here, I demonstrate some basic MCMC methods to get started with Bayesian techniques using a straightforward Gibbs/Metropolis-Hastings algorithm. Explore the code and gain a better understanding of Bayesian inference!

The following code was developed for an intensive course on Bayesian VAR Models for Macroeconometrics held at the School of Economics and Business, University of Ljubljana.

The course is sponsored and co-organized by Banka Slovenije and is open not only to PhD students but also to economists and researchers from central banks. This session brought together over 30 participants, including representatives from Banka Slovenije and 11 other central banks, reflecting the broad interest and practical relevance of Bayesian VAR modeling in policy analysis.

1. Basic code for estimating Linear Regression under Gaussian Error, three algorithms are implemented, code.