Detecting Granular Time Series in Large Panels
71 Pages Posted: 22 Sep 2017 Last revised: 31 Jul 2018
Date Written: July 13, 2018
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: Granularity, Network Models, Factor Models, Panel Data, Industrial Production, CDS Spreads
JEL Classification: C33, C38
Suggested Citation: Suggested Citation