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The application of state-of-the-art spatial econometric models requires thatthe information about the spatial coordinates of statistical units iscompletely accurate, which is usually the case in the context of areal data.With micro-geographic point-level data, however, such information is inevitablyaffected by locational errors, that can be generated intentionally by the dataproducer for privacy protection or can be due to inaccuracy of the geocodingprocedures. This unfortunate circumstance can potentially limit the use of thespatial econometric modelling framework for the analysis of micro data. Indeed,some recent contributions (see e.g. Arbia, Espa and Giuliani 2016) have shownthat the presence of locational errors may have a non-negligible impact on theresults. In particular, wrong spatial coordinates can lead to downward bias andincreased variance in the estimation of model parameters. This contributionaims at developing a strategy to reduce the bias and produce more reliableinference for spatial econometrics models with location errors. The validity ofthe proposed approach is assessed by means of a Monte Carlo simulation studyunder different real-case scenarios. The study results show that the method ispromising and can make the spatial econometric modelling of micro-geographicdata possible.

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