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A non-parametric spatial independence test using symbolic entropy

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  • López, Fernando
  • Matilla-García, Mariano
  • Mur, Jesús
  • Marín, Manuel Ruiz

Abstract

In the present paper, we construct a new, simple, consistent and powerful test for spatial independence, called the SG test, by using the new concept of symbolic entropy as a measure of spatial dependence. The standard asymptotic distribution of the test is an affine transformation of the symbolic entropy under the null hypothesis. The test statistic, with the proposed symbolization procedure, and its standard limit distribution have appealing theoretical properties that guarantee the general applicability of the test. An important aspect is that the test does not require specification of the W matrix and is free of a priori assumptions. We include a Monte Carlo study of our test, in comparison with the well-known Moran's I, the SBDS (de Graaff et al., 2001) and [tau] test (Brett and Pinkse, 1997) that are two non-parametric tests, to better appreciate the properties and the behaviour of the new test. Apart from being competitive compared to other tests, results underline the outstanding power of the new test for non-linear dependent spatial processes.

Suggested Citation

  • López, Fernando & Matilla-García, Mariano & Mur, Jesús & Marín, Manuel Ruiz, 2010. "A non-parametric spatial independence test using symbolic entropy," Regional Science and Urban Economics, Elsevier, vol. 40(2-3), pages 106-115, May.
  • Handle: RePEc:eee:regeco:v:40:y:2010:i:2-3:p:106-115
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    References listed on IDEAS

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    Cited by:

    1. Fernando A. López & Mariano Matilla-García & Jesús Mur & Antonio Páez & Manuel Ruiz, 2016. "A note on the SG(m) test," Journal of Geographical Systems, Springer, vol. 18(1), pages 87-96, January.
    2. Richard Harris & John Moffat & Victoria Kravtsova, 2011. "In Search of ‘ W ’," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(3), pages 249-270, February.
    3. Sensoy, Ahmet & Fabozzi, Frank J. & Eraslan, Veysel, 2017. "Predictability dynamics of emerging sovereign CDS markets," Economics Letters, Elsevier, vol. 161(C), pages 5-9.
    4. 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.
    5. Sensoy, Ahmet & Aras, Guler & Hacihasanoglu, Erk, 2015. "Predictability dynamics of Islamic and conventional equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 222-248.
    6. Sensoy, Ahmet, 2019. "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, vol. 28(C), pages 68-73.
    7. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    8. 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.
    9. Elsinger, Helmut, 2013. "Comment on: A non-parametric spatial independence test using symbolic entropy," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 838-840.
    10. Herrera Gómez, Marcos, 2010. "Causalidad Espacial. Enfoque No Paramétrico [Spatial Causality. Non-Parametric Approach]," MPRA Paper 61326, University Library of Munich, Germany.
    11. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús, 2011. "Detección de Dependencia Espacial mediante Análisis Simbólico [Detection of Spatial Dependence using Symbolic Analysis]," MPRA Paper 38603, University Library of Munich, Germany.
    12. Luc Anselin & Xun Li, 2019. "Operational local join count statistics for cluster detection," Journal of Geographical Systems, Springer, vol. 21(2), pages 189-210, June.
    13. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-17, April.
    14. Herrera Gómez, Marcos & Mur Lacambra, Jesús & Ruiz Marín, Manuel, 2012. "Selecting the Most Adequate Spatial Weighting Matrix:A Study on Criteria," MPRA Paper 73700, University Library of Munich, Germany.
    15. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús & Paelinck, Jean, 2010. "A Non-Parametric Approach to Spatial Causality," MPRA Paper 36768, University Library of Munich, Germany.
    16. López-Hernández , Fernando A. & Artal-Tur, Andrés & Maté-Sánchez-Val, M. Luz, 2011. "Identifying nonlinear spatial dependence patterns by using non-parametric tests: Evidence for the European Union," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 19-36.
    17. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.

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