arXiv 19 Oct 2024 · Econometrics · 3 citations (OpenAlex)
arXiv:2410.15090 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lütkepohl H, Shang F, Uzeda L, Woźniak T (2024) Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference | 0.874 | 11 | 2 | 100% |
| 2 | Waggoner DF, Zha T (2003) A Gibbs sampler for structural vector autoregressions | 0.874 | 7 | 2 | 100% |
| 3 | Chan JCC, Koop G, Yu X (2024) Large Order-Invariant Bayesian VARs with Stochastic Volatility | 0.874 | 5 | 2 | 100% |
| 4 | Kilian L, Lütkepohl H (2017) Structural Vector Autoregressive Analysis | 0.811 | 4 | 2 | 100% |
| 5 | Giannone D, Lenza M, Primiceri GE (2015) Prior selection for Vector Autoregressions | 0.737 | 3 | 2 | 100% |
| 6 | Lütkepohl H, Woźniak T (2020) Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity | 0.737 | 3 | 2 | 100% |
| 7 | Lanne M, Lütkepohl H (2010) Structural Vector Autoregressions With Nonnormal Residuals | 0.737 | 3 | 2 | 100% |
| 8 | Lanne M, Meitz M, Saikkonen P (2017) Identification and estimation of non-Gaussian structural vector autoregressions | 0.737 | 3 | 2 | 100% |
| 9 | Malsiner-Walli G, Frühwirth-Schnatter S, Grün B (2016) Model-based clustering based on sparse finite Gaussian mixtures | 0.737 | 3 | 2 | 100% |
| 10 | Arias JE, Rubio-Ramírez JF, Waggoner DF (2018) Inference Based on Structural Vector Autoregressions Identified with Sign and Zero Restrictions: Theory and Applications | 0.644 | 2 | 2 | 100% |
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