Bayesian VAR (BVAR) with Conjugate Priors

A Python implementation of a standard Bayesian Vector Autoregression (VAR) with conjugate Normal–Inverse–Wishart priors and Minnesota hyperparameter structure. This repository provides routines for:

• Specifying and estimating a VAR($p$) model
• Constructing Minnesota priors with five hyperparameters ($\lambda_0$ – $\lambda_5$)
• Sampling from the Normal–Inverse–Wishart posterior
• Computing Monte Carlo impulse response functions (IRFs)
• Performing forecast error variance decomposition (FEVD)

Data:

The data used to run the model (to recreate the results) is located in the repository under the name Tes-Bills Final.xlsx, the code should work with more variables and different types of time series without major complication

Features

• Hyperparameter optimization via closed-form marginal likelihood
• Posterior sampling of coefficients and covariance draws
• IRF & FEVD routines with companion-form and Cholesky factorization
• Flexible handling of lag order $p$ and variable ordering
• Example scripts analyzing shock transmission between U.S. 5-year and 1-year T-Bills and Colombian 5-years TES

Dependencies

• Python 3.7+
• pandas
• numpy
• scipy
• matplotlib

Install via:

pip install pandas numpy scipy matplotlib

References:

J. Jacobo, Una introducción a los métodos de máxima entropía y de inferencia bayesiana en econometría

Contributing

Contributions are welcome! Please open issues or submit pull requests at
https://github.com/pablo-reyes8

License

This project is licensed under the Apache License 2.0.