Herrera Gómez, Marcos and Ruiz Marín, Manuel and Mur Lacambra, Jesús (2011): Detección de Dependencia Espacial mediante Análisis Simbólico.
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Abstract
Testing for the assumption of independence between spatial variables is an important first step in spatial conometrics. Usually the researchers use the bivariate generalization of the Moran’s statistic, specifying a spatial matrix a priori. This test is applicable only to detect linear relations in pairs of variables, which must be spatially non-autocorrelated. We develop a new non-parametric test, based on symbolic dynamics, that is free of these shortcomings. The test is consistent, computationally simple to obtain and powerful as shown in our Monte Carlo experiment.
Item Type: | MPRA Paper |
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Original Title: | Detección de Dependencia Espacial mediante Análisis Simbólico |
English Title: | Detection of Spatial Dependence using Symbolic Analysis |
Language: | Spanish |
Keywords: | Contraste Bivariante de Moran, Dinámica Simbólica, Entropía Simbólica |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General |
Item ID: | 38603 |
Depositing User: | marcos herrera |
Date Deposited: | 07 May 2012 14:27 |
Last Modified: | 10 Oct 2019 04:39 |
References: | Anselin, L. (1988). Spatial Econometrics. Methods and Models. Kluwer Academic, Dordrecht. Bivand, R. (1980). “A Monte Carlo Study of Correlation Coefficient Estimation with Spatially Correlated Observations”, Quaestiones Geographicae, 6, pp.5-10. Cerioli, A. (1997). “Modified Tests of Independence in 2x2 Tables with Spatial Data”, Biometrics, 53, pp. 619-628. Cliff, A. y K. Ord (1981). Spatial Processes: Models and Applications. Pion, London. Clifford, P. y S. Richardson (1985). “Testing for the Association between Two Spatial Processes”, Statistics and Decissions, Suppl., 2, pp. 155-160. Cressie, N. (1993). Statistics for Spatial Data (revised version). John Wiley & Sons, New York. Czaplewski, R. y R. Reich (1993). “Expected Value and Variance of Moran’s I Bivariate Spatial Autocorrelation Statistic for a Permutation Test”. USDA, Forest Service. Research Paper RM-309. Haining, R. (1991). “Bivariate Correlation with Spatial Data”, Geographical Analysis, 23, pp. 210-227. Haining, R. (2003). Spatial Data Analysis. Theory and Practice. Cambridge University Press, Cambridge. Hao, B. y W. Zheng (1998). Applied symbolic dynamics and chaos. World Scientific, Singapore. Herrera, M. (2011). Causality. Contributions to Spatial Econometrics. Ph.D Thesis, Universidad de Zaragoza(España). Disponible en https://sites.google.com/site/spatialcausality/ Hong, Y. y H. White (2005). “Asymptotic distribution theory for nonparametric entropy measures of serial dependence”, Econometrica, 73, pp. 837-901. LeSage, J. y K. Pace (2009). Introduction to Spatial Econometrics. Chapman & Hall/CRC, Boca Raton. López, F., Matilla-García, M., Mur, J. y M. Ruiz Marín(2010). “A non-parametric spatial independence test using symbolic entropy”, Regional Science and Urban Economics, 40, pp. 106-115. Mantel, N. (1967). “The Detection of Disease Clustering and a Generalized Regression Approach”, Cancer Research, 27, pp. 209-220. Matilla-García, M. y M. Ruiz Marín (2008). “A Non-parametric Independence Test Using Permutation Entropy”, Journal of Econometrics, 144, pp. 139-155. Matilla-García, M. y M. Ruiz Marín (2009). “Detection of Non-linear Structure in Time Series”, Economics Letters, 105, pp. 1-6. Ruiz, M., López, F. y A. Páez (2009). “Testing for Spatial Association of Qualitative Data Using Symbolic Dynamics”, Journal of Geographical Systems, 10.1007/s10109-009-0100-1. Soon, S. (1996). “Binomial Approximation for Dependent Indicators”, Statistica Sinica, 6, pp. 703–714. Wartenberg, D. (1985). “Multivariate Spatial Corrrelation: A Method for Exploratory Geographical Analysis”, Geographical Analysis, 17, pp. 263-283. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/38603 |