mlsanityData to 14 Oct 2026

Price Stability in the European Union: A Systemic Approach Using Random Matrix Theory

arXiv 2609.35011 ↗First posted 28 Sep 2026cs.LG econ.EM stat.APcited by 0

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Abstract (arXiv)

Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions. Mainstream economics tried to leverage price stability using price indices and several metrics to shed light on specific dynamics and optimal macroeconomic levels. The wide availability of data led researchers to consider the study of systems using Random Matrix Theory, based on inner correlation patterns. This aims to enhance the multivariate analysis by removing noisy patterns from the signal and improve data quality for further inferences. This work considers the collection of monthly inflation indices in the Eurozone as a textitsystem of prices to analyze its eigenvalues' statistical and asymptotic properties and uncover inner country-level insights. Results confirm the system cannot assumed to be randomly generated, and the data exhibit noise-dominated patterns, due to small and persistent variations at the country-level. The latter make the inter-country correlations more dynamic and the separation of the signal from the noise quiet difficult. Findings identified two countries as distorting inflation dynamics besides three other distinct, regional-based groups of countries. Variability sources might stem from economic episodes fueling inflation spikes in some countries, as well as methodological aspects used to ensure data quality and representativeness in the European Union. Despite being complex, the system demonstrates a certain stability, in terms of self-organization; while large monthly fluctuations cannot be considered as rare events, but part of the data-generating process.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 28 Sep 2026.

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References · 4

  1. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 674
  2. 2018Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learningcited by 215
  3. 2020Predicting trends in the quality of state-of-the-art neural networks without access to training or testing datacited by 115
  4. 2025SETOL: A Semi-Empirical Theory of (Deep) Learningcited by 12

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