论文标题
常规晶格数据的空间加权矩阵的估计 - 一种自适应套索方法，横截面重新采样
Estimation of the spatial weighting matrix for regular lattice data -- An adaptive lasso approach with cross-sectional resampling
论文作者
论文摘要
空间计量经济学研究通常取决于以下假设：空间依赖性结构是事先知道的，并由确定性的空间重量矩阵表示。与经典方法相反，我们研究了常规晶格数据的稀疏空间依赖性结构的估计。特别是，使用自适应的绝对收缩和选择算子（LASSO）用于选择和估计空间重量矩阵的各个连接。为了恢复空间依赖性结构，假设随机过程是可以交换的，我们提出了横截面重采样。估计程序基于两步方法，以规避同时发生问题，该问题通常是由内源性空间自回旋依赖性引起的。使用蒙特卡洛模拟验证了带有横截面重采样的两步自适应拉索方法。最终，我们将程序应用于建模二氧化氮（$ \ MATHRM {NO_2} $）浓度，并表明估计与使用预指定权重矩阵相反的空间依赖结构可大大提高预测准确性。
Spatial econometric research typically relies on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix. Contrary to classical approaches, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particular, an adaptive least absolute shrinkage and selection operator (lasso) is used to select and estimate the individual connections of the spatial weights matrix. To recover the spatial dependence structure, we propose cross-sectional resampling, assuming that the random process is exchangeable. The estimation procedure is based on a two-step approach to circumvent simultaneity issues that typically arise from endogenous spatial autoregressive dependencies. The two-step adaptive lasso approach with cross-sectional resampling is verified using Monte Carlo simulations. Eventually, we apply the procedure to model nitrogen dioxide ($\mathrm{NO_2}$) concentrations and show that estimating the spatial dependence structure contrary to using prespecified weights matrices improves the prediction accuracy considerably.
