Speaker
Description
The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast and efficient estimation of these multivariate dynamic structural models, possibly with many variables, complex identification strategies, and non-linear characteristics. The models can be identified using adjustable exclusion restrictions and heteroskedastic or non-normal shocks. They feature a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, statistical verification of identification and hypotheses on autoregressive parameters, and analyses of structural shocks, volatilities, and fitted values. These features differentiate bsvars from existing R packages that either focus on a specific structural model, do not consider heteroskedastic shocks, or lack the implementation using compiled code.
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Additional Material or Paper
Paper: https://doi.org/10.48550/arXiv.2410.15090
Package: https://cran.r-project.org/package=bsvars
Website: https://bsvars.org/
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | dynamic modelling, structural modelling, predictive analytics, dynamic causal effects, Bayesian statistics |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Material License | My package and materials are distributed under GPL (≥ 3). The conference materials need not be. |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |