A Family of Correlated Observations: From Independent to Strongly Interrelated Ones

Daniel A. Griffith
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Daniel A. Griffith: School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA

Stats, 2020, vol. 3, issue 3, 1-19

Abstract: This paper proposes a new classification of correlated data types based upon the relative number of direct connections among observations, producing a family of correlated observations embracing seven categories, one whose empirical counterpart currently is unknown, and ranging from independent (i.e., no links) to approaching near-complete linkage (i.e., n(n − 1)/2 links). Analysis of specimen datasets from publicly available data sources furnishes empirical illustrations for these various categories. Their descriptions also include their historical context and calculation of their effective sample sizes (i.e., an equivalent number of independent observations). Concluding comments contain some state-of-the-art future research topics.

Keywords: correlated data; social network series; space series; time series; space-time series (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:3:y:2020:i:3:p:14-184:d:378101

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