We present an estimation procedure of spatial and temporal effects in spatiotemporal autoregressive panel data models using the Least Absolute Shrinkage and Selection Operator, LASSO (Tibshirani, 1996). We assume that the spatiotemporal panel is drawn from a univariate random process and that the data follows a spatiotemporal autoregressive process which includes a regressive term with space-/ time-varying exogenous regressor, a temporal autoregressive term and a spatial autoregressive term with an unknown weights matrix. The aim is to estimate this weight matrix alongside other parameters using a constraint penalised maximum likelihood estimator. Monte Carlo simulations showed a good performance with the accuracy increasing with an increasing number of time points. The use of the LASSO technique also consistently distinguishes between meaningful relationships (non-zeros) from those that are not (existing zeros) in both the spatial weights and other parameters. This regularised estimation procedure is applied to hourly particulate matter concentrations (PM10) in the Bavaria region, Germany for the years 2005 to 2020. Results show some stations with a high spatial dependency, resulting in a greater influence of PM10 concentrations in neighbouring monitoring stations. The LASSO technique proved to produce a sparse weights matrix by shrinking some weights to zero, hence improving the interpretability of the PM concentration dependencies across measurement stations in Bavaria

翻译：本文提出一种利用最小绝对收缩与选择算子（LASSO，Tibshirani, 1996）估计时空自回归面板数据模型中空间与时间效应的程序。我们假设时空面板数据来源于单变量随机过程，且数据遵循时空自回归过程，该过程包含具有空间/时间变化外生回归量的回归项、时间自回归项以及带有未知权重矩阵的空间自回归项。目标是通过约束惩罚最大似然估计量同时估计该权重矩阵及其他参数。蒙特卡洛模拟显示该方法性能良好，且估计精度随时间点数量增加而提升。LASSO技术能稳定区分空间权重与其他参数中有意义的关联（非零值）与无意义关联（存在的零值）。该正则化估计程序应用于德国巴伐利亚地区2005年至2020年的小时颗粒物浓度（PM10）数据。结果显示部分监测站具有较高的空间依赖性，导致邻近监测站的PM10浓度对其产生更大影响。LASSO技术通过将部分权重收缩至零，生成稀疏的权重矩阵，从而提升了巴伐利亚地区各测量站点间PM浓度依赖关系的可解释性。