欧盟价格稳定性：基于随机矩阵理论的系统性方法

中文摘要 AI 辅助

价格稳定性仍然是货币政策实践中的支柱，并在货币联盟中具有特殊重要性。主流经济学试图利用价格指数和多种指标来利用价格稳定性，以揭示特定动态和最优宏观经济水平。数据的广泛可用性促使研究人员考虑基于内部相关模式的随机矩阵理论来研究系统。这旨在通过从信号中去除噪声模式来增强多变量分析，并提高数据质量以供进一步推断。本研究将欧元区月度通胀指数的集合视为一个价格“系统”，分析其特征值的统计和渐近性质，并揭示国家层面的内部见解。结果证实，该系统不能被视为随机生成的，数据表现出噪声主导的模式，这是由于国家层面小而持续的变化。后者使国家间相关性更具动态性，使得信号与噪声的分离相当困难。研究发现，两个国家扭曲了通胀动态，此外还有三个不同的、基于区域的国家群体。变异来源可能源于推动某些国家通胀飙升的经济事件，以及用于确保欧盟数据质量和代表性的方法论方面。尽管复杂，该系统在自组织方面表现出一定的稳定性；而大的月度波动不能被视为罕见事件，而是数据生成过程的一部分。

英文摘要

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 \textit{system} 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.

发表机构

• Universität Hamburg（汉堡大学）

机构由 AI 辅助整理，请以论文原文为准。