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