Measuring the Graph Concordance of Locally Dependent Observations

Kyungchul Song
Additional contact information
Kyungchul Song: Vancouver School of Economics, University of British Columbia

The Review of Economics and Statistics, 2018, vol. 100, issue 3, 535-549

Abstract: This paper introduces a simple measure of a concordance pattern among observed outcomes along a network, that is, the pattern in which adjacent outcomes tend to be more strongly correlated than nonadjacent outcomes. The graph concordance measure can be generally used to quantify the empirical relevance of a network in explaining cross-sectional dependence of the outcomes, and as shown in the paper, it can also be used to quantify the extent of homophily under certain conditions. When one observes a single large network, it is nontrivial to make inferences about the concordance pattern. Assuming a dependency graph, this paper develops a permutation-based confidence interval for the graph concordance measure. The confidence interval is valid in finite samples when the outcomes are exchangeable, and under the dependency graph, an assumption together with other regularity conditions, is shown to exhibit asymptotic validity. Monte Carlo simulation results show that the validity of the permutation method is more robust than the asymptotic method to various graph configurations.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.mitpressjournals.org/doi/pdf/10.1162/rest_a_00714 (application/pdf)
Access to PDF is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tpr:restat:v:100:y:2018:i:3:p:535-549

Ordering information: This journal article can be ordered from
https://mitpressjour ... rnal/?issn=0034-6535

Access Statistics for this article

The Review of Economics and Statistics is currently edited by Pierre Azoulay, Olivier Coibion, Will Dobbie, Raymond Fisman, Benjamin R. Handel, Brian A. Jacob, Kareen Rozen, Xiaoxia Shi, Tavneet Suri and Yi Xu

More articles in The Review of Economics and Statistics from MIT Press
Bibliographic data for series maintained by The MIT Press ().