Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data

The application of spatial Cliff-Ord models requires information about spatial coordinates of statistical units to be reliable, which is usually the case in the context of arealdata. With micro-geographic point-level data, however, such information is inevitably affected by locational errors, that can be generated intentionally by the data producerfor privacy protection or can be due to inaccuracy of the geocoding procedures. This unfortunate circumstance can potentially limit the use of the spatial autoregressive modelling framework for the analysis of micro data, as the presence of locational errors may have a non-negligible impact on the estimates of model parameters. This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by meansof a marked point process. The model is fitted through the maximisation of a doubly-marginalised likelihood function of the marked point process, which cleans out theeffects of coarsening. The validity of the proposed approach is assessed by meansof a Monte Carlo simulation study under different real-case scenarios, whereas it isapplied to real data on house prices.