juila implementations of Bayesian Vector Autoregressive (BVAR) framework

Toolkit julia source codes and example outputs for Bayesian (Structural) Vector Autoregressive (VAR) models. There are identification strategies in multivariabe time series analysis that requires bayesian framework, such as,

• Dynamic probabilistic forecasting estimations
• Stochastic volatility
• Time-varying parameters
• 'Big-data' or large dimension models
• Structural identification (partial-equilibrium)

A lot of the research and source codes are mainly written in MATLAB. The purpose of this repository is to direct-transalte those source codes from academic research and codes publicly available into open-sourced julia programming languge. This repository does not claim original authorship of the algorithms translated and used, and I recommend users to see the original research cited below.

This repository also includes applications of the models, such as estimation of impulse responses, foreasting, and scenarios/simulations.

Contents of source code

Source code	Model framework	References
bar-sv	Bayesian AR application with state-space stochastic volatility	Mein: technically not multivariate but a good baseline use case
bsts	Bayesian Structural Time Series model	Mein: technically not multivariate but a good baseline use case
bvar	Bayesian VAR model with Gibbs sampling (Minnesota-prior)	Gary Koop and Dimitris Korobilis replication of Christiano et al. (2016)
bvar-vp	Bayesian VAR model with varying prior for extreme episodes	Cascaldi-Garcia - Pandemic prior
tvp-var	Time-varying parameter VAR with stochastic volatility (Code directly available; application example of macroeconomic consumer sentiment)	Harron Mumtaz replication code
bh-bsvar	Bayesian Structural Vector Autoregressive Model with Sign Restriction	Baumeister and Hamilton (2015) replication code
mf-var	Mixed-frequency VAR model	Harron Mumtaz replication code
dfm	Dynamic Factor Model