arXiv 28 Jan 2025 · Econometrics
arXiv:2501.16711 · PDF · DOI · OpenAlex · Extracted main text
The R package bsvarSIGNs implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. It offers fast and efficient estimation thanks to the deployment of frontier econometric and numerical techniques and algorithms written in C++. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors. The structural model can be identified by sign, zero, and narrative restrictions, including a novel solution, making it possible to use the three types of restrictions at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The package was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
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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 | Arias JE, Rubio-Ramírez JF, Waggoner DF (2018) Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications | 0.874 | 9 | 2 | 100% |
| 2 | Giannone D, Lenza M, Primiceri GE (2015) Prior Selection for Vector Autoregressions | 0.874 | 6 | 2 | 100% |
| 3 | Doan T, Litterman RB, Sims CA (1984) Forecasting and Conditional Projection Using Realistic Prior Distributions | 0.811 | 4 | 2 | 100% |
| 4 | Woźniak T (2024) bsvars: Bayesian Estimation of Structural Vector Autoregressive Models | 0.811 | 4 | 2 | 100% |
| 5 | Antolín-Díaz J, Rubio-Ramírez JF (2018) Narrative Sign Restrictions for SVARs | 0.737 | 3 | 2 | 100% |
| 6 | Wang X, Woźniak T (2024) bsvarSIGNs: Bayesian SVARs with Sign, Zero, and Narrative Restrictions | 0.644 | 2 | 2 | 100% |
| 7 | Woźniak T (2016) Bayesian Vector Autoregressions | 0.644 | 2 | 2 | 100% |
| 8 | Rubio-Ramírez JF, Waggoner DF, Zha T (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference | 0.585 | 3 | 1 | 100% |
| 9 | Kilian L, Lütkepohl H (2017) Structural Vector Autoregressive Analysis | 0.511 | 2 | 1 | 100% |
| 10 | Woźniak T (2024) Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.