Miguel Sanchez-Martinez, Tomasz Woźniak
arXiv 12 Jun 2026 · Econometrics
arXiv:2606.14143 · PDF · DOI · OpenAlex · Extracted main text
The R package bpvars was designed to forecast employment, unemployment, and labour market participation rates of 189 countries. However, it is generally applicable to dynamic panel data due to the flexibility of its modelling framework and robust coding. It includes a family of Bayesian hierarchical panel Vector Autoregressions (VARs) that are characterised by: (i) country-specific VAR models (ii) with their parameters' priors centred around their global counterparts, and (iii) featuring flexible multi-level hierarchical prior distributions (iv) with many variants of well-established in the literature benchmark choices, and (v) four alternative specifications including groupping of country-specific or global parameters. A distinguishing feature is its implementation of missing observation treatment based on a model-coherent Bayesian approach. These models are accompanied by Bayesian prediction, offering a wide range of possible specifications that aim to increase forecasting precision and comply with various reporting standards. We also implement pseudo-out-of-sample recursive forecasting for evaluating point and density forecast performance. The package implements model specification, estimation, and forecasting routines, facilitating simple workflows and reproducibility, including estimation and forecasting results summaries and visualisations. It achieves extraordinary computational speed thanks to the employment of frontier econometric and numerical techniques, as well as algorithms written in C++.
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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 | Jarociński M (2010) Responses to monetary policy shocks in the east and the west of Europe: a comparison | 1.000 | 6 | 3 | 100% |
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| 3 | Karlsson S (2013) Forecasting with Bayesian Vector Autoregression | 0.843 | 3 | 3 | 100% |
| 4 | Zellner A, Hong C (1989) Forecasting international growth rates using Bayesian shrinkage and other procedures | 0.843 | 3 | 3 | 100% |
| 5 | Sims CA (1980) Macroeconomics and Reality | 0.644 | 2 | 2 | 100% |
| 6 | Woźniak T (2025) bpvars: Forecasting with Bayesian Panel Vector Autoregressions | 0.644 | 2 | 2 | 100% |
| 7 | Waggoner DF, Zha T (1999) Conditional Forecasts in Dynamic Multivariate Models | 0.511 | 2 | 1 | 100% |
| 8 | Woźniak T (2016) Bayesian Vector Autoregressions | 0.511 | 2 | 1 | 100% |
| 9 | Dieppe A, van Roye B (2024) The BEAR toolbox | 0.405 | 1 | 1 | 100% |
| 10 | Boeck M, Feldkircher M, Huber F (2025) BGVAR: Bayesian Global Vector Autoregressions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 58 scored citations.