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A Design Concept of Forecasting Software for Normalized Vector Autoregressions with Fat Tails and Stochastic Volatility — arXiv:2608.28087

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28087
License (as archived): CC0-1.0
Archived: 2026-10-17T11:17:36.893Z
SHA-256 of the archived text: 0a597e440cb0e8078ac427d416c7c2893688e7b32ffb1ae03133993c518789ed
Stable URL of this page: https://metraipolis.com/documento/dc_4d0799fcd4bc

How to cite

A Design Concept of Forecasting Software for Normalized Vector Autoregressions with Fat Tails and Stochastic Volatility — arXiv:2608.28087 — arXiv (en), https://arxiv.org/abs/2608.28087 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4d0799fcd4bc (Metraipolis archive dc_4d0799fcd4bc, sha256 0a597e440cb0e8078ac427d416c7c2893688e7b32ffb1ae03133993c518789ed)

BibTeX

@misc{dc4d0799fcd4bc,
  title = {A Design Concept of Forecasting Software for Normalized Vector Autoregressions with Fat Tails and Stochastic Volatility — arXiv:2608.28087},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28087},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4d0799fcd4bc; sha256 0a597e440cb0e8078ac427d416c7c2893688e7b32ffb1ae03133993c518789ed},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28087. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present a suite of R packages for macroeconomic forecasting that leverages advanced Bayesian, structural, multivariate, dynamic, hierarchical, non-linear, and non-Gaussian models. The suite enables both structural and predictive analyses, and is adapted to time series data across various types, dimensions, and sampling frequencies. Each additional feature increases computational complexity. To address this challenge, our software design incorporates a carefully curated selection of models, efficient algorithms implemented in C++, advanced econometric and numerical methods, robust handling of complex input and output objects, and standardised workflows. This approach combines the computational efficiency of C++ with the convenience of working with data in R. We demonstrate that our packages facilitate original research contributions in forecasting, as illustrated by our example in which vector autoregressions with non-centred stochastic volatility enhance density and point predictions relative to models with centred stochastic volatility.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-31 in stat.CO).
Categorie arXiv: Econometrics (econ.EM) ; Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2608.28087 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:58:00.675Z dalla pagina https://arxiv.org/catchup/stat.CO/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).