Estimation of Spatial and Temporal Autoregressive Effects Using LASSO—An Example of Hourly Particulate Matter Concentrations

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. 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, along with other parameters, using a constraint‐penalized maximum likelihood estimator. Monte Carlo simulations showed good performance, with accuracy increasing as the number of time points increased. The use of the LASSO technique also consistently distinguishes between meaningful relationships (non‐zeros) and those that are not (existing zeros) in both the spatial weights and other parameters. This regularized estimation procedure is applied to hourly particulate matter concentrations (PM 10$$ {\kern0em }_{10} $$) 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 PM concentrations in neighboring monitoring stations. The LASSO technique proved to produce a sparse weight matrix by shrinking some weights to zero, hence improving the interpretability of the PM 10$$ {\kern0em }_{10} $$ concentration dependencies across measurement stations in Bavaria.