scholarly journals Building W Matrices Using Selected Geostatistical Tools: Empirical Examination and Application

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Elżbieta Antczak
Keyword(s):  
Spatial Data ◽  
Spatial Dependence ◽  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Spatial Data Analysis ◽  
Spatial Dependency ◽  
Empirical Examination ◽  
Spatial Panel ◽  
Spatial Weights ◽  
Unrelated Regression

This paper investigates how to determine the values (elements) of spatial weights in a spatial matrix (W) endogenously from the data. To achieve this goal, geostatistical tools (standard deviation ellipsis, semivariograms, semivariogram clouds, and surface trend models) were used. Then, in the econometric part of the analysis, the effect of applying different variants of matrices was examined. The study was conducted on a sample of 279 Polish towns from 2005–2015. Variables were related to the quantity of produced waste and economic development. Both exploratory spatial data analysis and estimations of spatial panel and seemingly unrelated regression models were performed by including particular W matrices in the study (exogenous-random as well as distance and directional matrices constructed based on data). The results indicated that (1) geostatistical tools can be effectively used to build Ws; (2) outcomes of applying different matrices did not exclude but supplemented one another, although the differences were significant; (3) the most precise picture of spatial dependence was achieved by including distance matrices; and (4) the values of the assessed parameter at the regressors did not significantly change, although there was a change in the strength of the spatial dependency.

scholarly journals Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

2020 ◽  
Vol 17 (24) ◽  
pp. 9193
Author(s):  
Chunshan Zhou ◽  
Rongrong Zhang ◽  
Xiaoju Ning ◽  
Zhicheng Zheng
Keyword(s):  
Influencing Factors ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Grain Production ◽  
Temporal Characteristics ◽  
Spatial Panel ◽  
Geographically Weighted Regression Model ◽  
Northern Areas ◽  
Spatial Panel Data ◽  
The Impact

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

Two-parameter ridge estimation in seemingly unrelated regression models

2020 ◽  
pp. 1-15
Author(s):  
Robab Mehdizadeh Esfanjani ◽  
Dariush Najarzadeh ◽  
Hossein Jabbari Khamnei ◽  
Farshin Hormozinejad ◽  
Mahnaz Talebi
Keyword(s):  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Ridge Estimation ◽  
Unrelated Regression ◽  
Two Parameter
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