In this paper, we propose a two-step lasso estimation approach to estimate the full spatial weights matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial locations. The proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. In addition, it is easy to compute the suggested estimators, because of a convex objective function resulting from a slight simplification. Via simulation studies, we show the finite-sample performance of the estimators and provide a practical guidance, when the approach could be applied. Eventually, the invented method is illustrated by an empirical example of regional monthly real-estate prices in Berlin from 1995 to 2014. The spatial units are defined by the respective ZIP codes. In particular, we can estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spill over effects.
翻译:在本文中,我们提出了一个分两步的拉索估计方法,以估计空间自动递减模型的全部空间加权矩阵。此外,我们允许每个空间位置的当地手段出现数目不详的结构性断裂。拟议方法共同估计空间依赖性、所有结构性断裂和当地平均水平。此外,由于轻微简化产生的曲线客观功能,很容易计算建议的估算器。Via模拟研究,我们展示了估计器的有限抽样性能,并在适用该方法时提供了实用指导。最终,从1995年到2014年柏林区域月度不动产价格的经验实例说明了所发明的方法。 空间单位由各自的ZIP代码界定。特别是,我们可以估计当地平均水平,并量化观察到的价格因空间溢出效应而偏离这些水平的情况。