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Detecting dependence between spatial processes

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Author Info

  • Herrera Gómez, Marcos
  • Ruiz Marín, Manuel
  • Mur Lacambra, Jesús

Abstract

Testing the assumption of independence between variables is a crucial aspect of spatial data analysis. However, the literature is limited and somewhat confusing. To our knowledge, we can mention only the bivariate generalization of Moran’s statistic. This test suffers from several restrictions: it is applicable only to pairs of variables, a weighting matrix and the assumption of linearity are needed; the null hypothesis of the test is not totally clear. Given these limitations, we develop a new non-parametric test based on symbolic dynamics with better properties. We show that the test can be extended to a multivariate framework, it is robust to departures from linearity, it does not need a weighting matrix and can be adapted to different specifications of the null. The test is consistent, computationally simple and with good size and power, as shown by a Monte Carlo experiment. An application to the case of the productivity of the manufacturing sector in the Ebro Valley illustrates our approach.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 43861.

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Date of creation: 2013
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Handle: RePEc:pra:mprapa:43861

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Related research

Keywords: Non-parametric methods; Spatial bootstrapping; Spatial independence; Symbolic dynamics;

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