bayesianVARs

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) doi:10.48550/arXiv.2206.04902. Efficient equation-per-equation estimation following Kastner & Huber (2020) doi:10.1002/for.2680 and Carrerio et al. (2021) doi:10.1016/j.jeconom.2021.11.010.

Last 30 days

This package has been downloaded 628 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 6,329 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Dec 05, 2024 with 87 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.