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Detecting Dependence Between Spatial Processes

Author

Listed:
  • Marcos Herrera
  • Manuel Ruiz
  • Jesús Mur

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, Υ( m ), based on symbolic dynamics with better properties. We show that the Υ( m ) 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.

Suggested Citation

  • Marcos Herrera & Manuel Ruiz & Jesús Mur, 2013. "Detecting Dependence Between Spatial Processes," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(4), pages 469-497, February.
    • Handle: RePEc:taf:specan:v:8:y:2013:i:4:p:469-497
    DOI: 10.1080/17421772.2013.835437
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    Cited by:

    1. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús, 2014. "Testing Spatial Causality in Cross-section Data," MPRA Paper 56678, University Library of Munich, Germany.
    2. Herrera Gómez, Marcos, 2013. "Análisis de Estructuras Espaciales Persistentes. Desempleo Departamental en Argentina [Persistent Spatial Structure Analysis. Regional Unemployment in Argentina]," MPRA Paper 49407, University Library of Munich, Germany.
    3. Marcos Herrera & Jesús Mur & Manuel Ruiz, 2016. "Detecting causal relationships between spatial processes," Papers in Regional Science, Wiley Blackwell, vol. 95(3), pages 577-594, August.
    4. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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