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Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

机译:广义空间面板数据模型中均匀和核高斯权重矩阵的比较

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摘要

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.
机译:面板数据结合了横截面数据和时间序列数据。如果横截面是位置,则需要检查位置之间的相关性。 ρ和λ是广义空间模型中的参数,用于覆盖位置之间相关性的影响。 ρ或λ的值会影响拟合模型的优劣,因此进行参数估计很重要。通过创建连续性矩阵,直到获得空间加权矩阵(W),即可覆盖另一个位置的影响。 W有几种类型-均匀W,二进制W,核高斯W和某些W,它们来自经济条件或运输条件的实际情况。本研究旨在使用RMSE值在空间面板数据模型中比较均匀W和核高斯W。分析结果表明,均匀权重的RMSE值小于核高斯模型。均匀W对所有组合均具有稳定值。

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