Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model
提出用自适应Lasso方法估计和选择空间权重矩阵的最佳线性组合，并加入稀疏调整矩阵，为空间计量模型提供统一框架，适用于地理距离或经济变量等多种设定。
Spatial econometric models allow for interactions among variables through the specification of a spatial weight matrix. Practitioners often face the risk of misspecification of such a matrix. In many problems a number of potential specifications exist, such as geographic distances, or various economic quantities among variables. We propose estimating the best linear combination of these specifications, added with a potentially sparse adjustment matrix. The coefficients in the linear combination, together with the sparse adjustment matrix, are subjected to variable selection through the adaptive least absolute shrinkage and selection operator (LASSO). As a special case, if no spatial weight matrices are specified, the sparse adjustment matrix becomes a sparse spatial weight matrix estimator of our model. Our method can therefore, be seen as a unified framework for the estimation and selection of a spatial weight matrix. The rate of convergence of all proposed estimators are determined when the number of time series variables can grow faster than the number of time points for data, while oracle properties for all penalized estimators are presented. Simulations and an application to stocks data confirms the good performance of our procedure.