Estimation of Spatial and Temporal Autoregressive Effects using LASSO - An Example of Hourly Particulate Matter Concentrations

TL;DR

This paper introduces a LASSO-based estimation method for identifying spatial and temporal effects in spatiotemporal autoregressive models, demonstrated on hourly particulate matter data in Bavaria, improving interpretability and accuracy.

Contribution

The paper develops a novel LASSO-based estimation procedure for spatiotemporal autoregressive models, including estimation of the weights matrix, with demonstrated effectiveness on real environmental data.

Findings

LASSO accurately distinguishes meaningful relationships from noise.

The method produces a sparse weights matrix, enhancing interpretability.

Application reveals high spatial dependency in PM10 concentrations.

Abstract

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