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 |
|---|---|
| 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 |

