The individual observations used to estimate spatial regression models often constitute only a sample of the theoretically observable data points. In many cases, such a sample does not obey a specific design and it is collected only with convenience criteria as it happens, e.g. when data are web scraped or crowdsourced. Thus, we expect to observe possible biases and inefficiencies while estimating the spatial regression parameters. In this paper, we present the results of various Monte Carlo experiments conducted to assess the extent of this problem in the estimation of a spatial econometric model. This assessment is done by isolating the effects because of the sample size, the pattern of the point distribution and sample criterion used in the data collection process. Furthermore, we suggest an approach based on Gibbs sampler that can be used to replace the unsampled data points. Our simulations and a real data case study confirm that our proposed strategy reduces the distorting effects produced by the sample observation, thus providing more reliable parameters’ estimations.

用于估计空间回归模型的单个观测值通常仅构成理论上可观测数据点的样本。在许多情况下，这样的样本不符合特定设计，并且仅在发生时根据便利标准进行收集，例如当数据被网络抓取或众包时。因此，我们期望在估计空间回归参数时观察可能的偏差和低效率。在本文中，我们展示了各种蒙特卡洛实验的结果，这些实验旨在评估该问题在空间计量经济模型估计中的严重程度。由于样本量、点分布模式和数据收集过程中使用的样本标准，该评估是通过隔离影响来完成的。此外，我们建议一种基于 Gibbs 采样器的方法，该方法可用于替换未采样的数据点。我们的模拟和真实数据案例研究证实，我们提出的策略减少了样本观察产生的扭曲效应，从而提供了更可靠的参数估计。