Spatial Econometric Model of Poverty in Java Island
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 420-425
Received: Aug. 26, 2015;
Accepted: Sep. 15, 2015;
Published: Sep. 26, 2015
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Mulugeta Aklilu Zewdie, Department of Statistics, Faculty of Natural and Computational Science, Mekelle University, Mekelle, Ethiopia
M. Nur Aidi, Department of Statistics, Faculty of Mathematics and Science, Bogor Agricultural University, Bogor, Indonesia
Bagus Sartono, Department of Statistics, Faculty of Mathematics and Science, Bogor Agricultural University, Bogor, Indonesia
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This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.
Poverty, Spatial Effects, Econometrics, Spatial Error Model, Special Lag Model, Spatial Durbin Model, LM, Robust LM, LRcom, Gauss-Markov &AIC
To cite this article
Mulugeta Aklilu Zewdie,
M. Nur Aidi,
Spatial Econometric Model of Poverty in Java Island, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 6,
2015, pp. 420-425.
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