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 671 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 59 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 6,332 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do 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.