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Detecting spatial homogeneity in the world trade web with Detrended Fluctuation Analysis

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  • Riccardo Chiarucci
  • Franco Ruzzenenti
  • Maria I. Loffredo

Abstract

In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links' weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link's weights) in a spatially embedded network, we propose a methodology based on DFA (Detrended Fluctuation Analysis). DFA is a well developed methodology to evaluate autocorrelations and estimate long-range behaviour in time series. We argue it can be further extended to spatially ordered series in order to assess autocorrelations in values. A scaling exponent of 0.5 (uncorrelated data) would thereby signal a perfect homogeneous space embedding the network. We apply the proposed methodology to the World Trade Web (WTW) during the years 1949-2000 and we find, in some contrast with predictions of gravity models, a declining influence of distances on trading relationships.

Suggested Citation

  • Riccardo Chiarucci & Franco Ruzzenenti & Maria I. Loffredo, 2013. "Detecting spatial homogeneity in the world trade web with Detrended Fluctuation Analysis," Papers 1308.0526, arXiv.org, revised Nov 2013.
    • Handle: RePEc:arx:papers:1308.0526
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    References listed on IDEAS

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    Cited by:

    1. Franco Ruzzenenti & Francesco Picciolo & Andreas Papandreou, 2015. "A network analysis of the global energy market: an insight on the entanglement between crude oil and the world economy," Papers 1509.05894, arXiv.org, revised Sep 2015.

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