Statistical inference for spatio-temporal autoregressive models of covariates with additive measurement errors

In this paper, we address the statistical inference problem proposed for the sparse spatio-temporal autoregressive models with additive measurement error when the number of spatial nodes exceeds the number of temporal observations. We use the improved Yule-Walker estimation method, adding the bagging algorithm to the estimation process to solve the over-identification problem. The simulation-extrapolation (SIMEX) method is used to reduce the influence of additive measurement error and we confirm the feasibility of empirical likelihood method to establish confidence intervals for model coefficients. Furthermore, some simulations and real examples are carried out to evaluate the finite sample performance.