Ignacio Moreira Lara, Jan Prüser, Christoph Hanck
arXiv 31 Jul 2026 · Econometrics
arXiv:2608.00262 · PDF · Extracted main text
Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework that exploits the natural matrix structure of international macroeconomic data. By separating dependence across economic variables from dependence across countries, the framework provides a parsimonious representation that substantially reduces the dimensionality of large structural systems. We develop a Bayesian sampling algorithm for posterior inference that accommodates zero, sign, and ranking (magnitude) restrictions, allowing established SVAR identification schemes to be combined with a novel approach to identifying contemporaneous international spillovers. Applying the model to quarterly data for 15 economies, we find substantial heterogeneity in international shock transmission, with demand shocks playing a more prominent role than supply shocks in generating cross-country spillovers.
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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 | Giannone, Domenico, Primiceri, Giorgio E (2025) Demand-Driven Inflation | 0.874 | 6 | 2 | 100% |
| 2 | Bergholt, Drago, Canova, Fabio, Furlanetto, Francesco, Maffei-Faccio… (2026) What Drives the Recent Surge in Inflation? The Historical Decomposition Roller Coaster | 0.737 | 3 | 2 | 100% |
| 3 | Chen, Rong, Xiao, Han, Yang, Dan (2021) Autoregressive Models for Matrix-Valued Time Series | 0.737 | 3 | 2 | 100% |
| 4 | Chang, Jui-Chuan Della, Jansen, Dennis W., Pagliacci, Carolina (2023) Inflation and Real GDP Growth in the U.S.–-Demand or Supply Driven? | 0.737 | 3 | 2 | 100% |
| 5 | Chan, Joshua CC, Qi, Yaling (2026) Large Bayesian matrix autoregressions | 0.693 | 7 | 1 | 100% |
| 6 | Read, Matthew, Zhu, Dan (2025) Fast Posterior Sampling in Tightly Identified SVARs Using `Soft' Sign Restrictions | 0.693 | 6 | 1 | 100% |
| 7 | Kilian, Lutz, Lütkepohl, Helmut (2017) Structural vector autoregressive analysis | 0.644 | 2 | 2 | 100% |
| 8 | Hou, Chenghan (2024) Large Bayesian SVARs with linear restrictions | 0.585 | 3 | 1 | 100% |
| 9 | Samadi, S Yaser, Billard, Lynne (2025) On a matrix-valued autoregressive model | 0.585 | 3 | 1 | 100% |
| 10 | Charnavoki, Valery, Dolado, Juan J (2014) The Effects of Global Shocks on Small Commodity-Exporting Economies: Lessons from Canada | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Impulse Response Inference for Matrix Autoregressions | 0.843 | 3 | 3 |