Detecting Granular Time Series in Large Panels

Geert Mesters and Christian Brownlees

No 991, Working Papers from Barcelona School of Economics

Abstract: Large economic and financial panels often contain time series that influence the entire cross-section. We name such series granular. In this paper we introduce a panel data model that allows to formalize the notion of granular time series. We then propose a methodology, which is inspired by the network literature in statistics and econometrics, to detect the set of granulars when such set is unknown. The influence of the i-th series in the panel is measured by the norm of the i-th column of the inverse covariance matrix. We show that a detection procedure based on the column norms allows to consistently select granular series when the cross-section and time series dimensions are large. Importantly, the methodology allows to consistently detect granulars also when the series in the panel are influenced by common factors. A simulation study shows that the proposed procedures perform satisfactorily in finite samples. Our empirical studies demonstrate, among other findings, the granular influence of the automobile sector in US industrial production.

Keywords: panel data; granularity; network models; factor models; industrial production; CDS spreads (search for similar items in EconPapers)
JEL-codes: C33 C38 (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Journal Article: Detecting granular time series in large panels (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:991

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