This paper proposes an alternative spatial weight that efficiently captures a spatial dependence. In the past, researchers often used sparse or inverse distance spatial weights. A dense spatial weight is defined by partitioning the separation distances between locations based on quantile values over large spatial scales, where each partition forms conjoint neighborhood sectors and the weights of the respective inverse quantile of separation distances are assigned to each sector. Instead of joint modeling of the spatiotemporal process, a simultaneous spatial panel model is employed after the panel component is imposed on the proposed spatial weight using Kronecker product to perform maximum likelihood estimation and Bayesian inference via MCMC Gibbs method. The specification also involves space, time, and space–time simultaneous components. The performance of the models for the proposed spatial weight is compared with the existing spatial weights using parameter bias. A smaller value of the bias close to zero indicates a stronger value of the spatial error parameter for the proposed spatial weight over the existing spatial weights. It also induces spatial auto-correlations both in the spatial panel data of neighboring locations as well as in the errors. Thus, the proposed method affirms the best efficiency for the dynamic combined spatial panel autoregressive model with random effect specification over the lag and error models, and fixed effect specification.

本文提出了一种有效捕获空间依赖性的替代空间权重。过去，研究人员经常使用稀疏或反距离空间权重。密集空间权重是通过基于大空间尺度上的分位数值来划分位置之间的分隔距离来定义的，其中每个分区形成联合邻域扇区，并且将分隔距离的相应反分位数的权重分配给每个扇区。代替时空过程的联合建模，在使用克罗内克积将面板分量施加到所提出的空间权重上之后，采用同步空间面板模型，以通过 MCMC Gibbs 方法执行最大似然估计和贝叶斯推理。该规范还涉及空间、时间和时空同时组成部分。使用参数偏差将所提出的空间权重的模型的性能与现有的空间权重进行比较。接近于零的较小偏差值指示所提出的空间权重的空间误差参数值比现有空间权重更强。它还会在相邻位置的空间面板数据以及误差中引起空间自相关。因此，所提出的方法肯定了具有随机效应规范的动态组合空间面板自回归模型相对于滞后和误差模型以及固定效应规范的最佳效率。接近于零的较小偏差值指示所提出的空间权重的空间误差参数值比现有空间权重更强。它还会在相邻位置的空间面板数据以及误差中引起空间自相关。因此，所提出的方法肯定了具有随机效应规范的动态组合空间面板自回归模型相对于滞后和误差模型以及固定效应规范的最佳效率。接近于零的较小偏差值指示所提出的空间权重的空间误差参数值比现有空间权重更强。它还会在相邻位置的空间面板数据以及误差中引起空间自相关。因此，所提出的方法肯定了具有随机效应规范的动态组合空间面板自回归模型相对于滞后和误差模型以及固定效应规范的最佳效率。