Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model
Affiliation(s)
Departement of Statistics, Graduate School of Bogor Agricultural University, Bogor, Indonesia.
Departement of Statistics, Graduate School of Bogor Agricultural University, Bogor, Indonesia.
ABSTRACT
Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.
Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.
Cite this paper
Purwaningsih, T. and , E. (2015) Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model. Open Journal of Statistics, 5, 90-95. doi: 10.4236/ojs.2015.51011.
Purwaningsih, T. and , E. (2015) Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model. Open Journal of Statistics, 5, 90-95. doi: 10.4236/ojs.2015.51011.
References
[1] Anselin, L., Gallo, J. and Jayet, H. (2008) The Econometrics of Panel Data. Springer, Berlin. [2] Aidi, M.N. and Purwaningsih, T. (2012) Modelling Spatial Ordinal Logistic Regression and the Principal Component to Predict Poverty Status of Districts in Java Island. International Journal of Statistics and Application, 3, 1-8. [3] Fotheringham, A.S., Brunsdon, C. and Chartlon, M. (2002) Geographically Weighted Regression, the Analysis of Spatially Varying Relationships. John Wiley and Sons, Ltd., Hoboken. [4] Elhorst, J.P. (2011) Spatial Panel Models. Regional Science and Urban Econometric. [5] Baltagi, B.H. (2005) Econometrics Analysis of Panel Data. 3rd Edition, John Wiley and Sons, Ltd., England. [6] Dubin, R. (2009) Spatial Weights. In: Fotheringham, A.S. and Rogerson, P.A., Eds., Handbook of Spatial Analysis, Sage Publications, London.
http://dx.doi.org/10.4135/9780857020130.n8 [7] Elhorst, J.P. (2010) Spatial Panel Data Models. In: Fischer, M.M. and Getis, A., Eds., Handbook of Applied Spatial Analysis, Springer, New York.
http://dx.doi.org/10.1007/978-3-642-03647-7_19
[1] Anselin, L., Gallo, J. and Jayet, H. (2008) The Econometrics of Panel Data. Springer, Berlin. [2] Aidi, M.N. and Purwaningsih, T. (2012) Modelling Spatial Ordinal Logistic Regression and the Principal Component to Predict Poverty Status of Districts in Java Island. International Journal of Statistics and Application, 3, 1-8. [3] Fotheringham, A.S., Brunsdon, C. and Chartlon, M. (2002) Geographically Weighted Regression, the Analysis of Spatially Varying Relationships. John Wiley and Sons, Ltd., Hoboken. [4] Elhorst, J.P. (2011) Spatial Panel Models. Regional Science and Urban Econometric. [5] Baltagi, B.H. (2005) Econometrics Analysis of Panel Data. 3rd Edition, John Wiley and Sons, Ltd., England. [6] Dubin, R. (2009) Spatial Weights. In: Fotheringham, A.S. and Rogerson, P.A., Eds., Handbook of Spatial Analysis, Sage Publications, London.
http://dx.doi.org/10.4135/9780857020130.n8 [7] Elhorst, J.P. (2010) Spatial Panel Data Models. In: Fischer, M.M. and Getis, A., Eds., Handbook of Applied Spatial Analysis, Springer, New York.
http://dx.doi.org/10.1007/978-3-642-03647-7_19