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Detecting granular time series in large panels

Christian Brownlees and Geert Mesters

Journal of Econometrics, 2021, vol. 220, issue 2, 544-561

Abstract: Large economic and financial panels can include 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 ith series in the panel is measured by the norm of the ith 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 study shows the granular influence of the automobile sector in US industrial production.

Keywords: Granularity; Network models; Factor models; Industrial production (search for similar items in EconPapers)
JEL-codes: C33 C38 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Detecting Granular Time Series in Large Panels (2017) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:220:y:2021:i:2:p:544-561

DOI: 10.1016/j.jeconom.2020.04.013

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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