Detecting dependence between spatial processes

Marcos Herrera Gómez, Manuel Ruiz Marin and Jesus Mur

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Non-parametric methods; Spatial bootstrapping; Spatial independence; Symbolic dynamics (search for similar items in EconPapers)
JEL-codes: C12 C15 C21 R12 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Detecting Dependence Between Spatial Processes (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:43861

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