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

TL;DR

This paper develops a new method to reduce bias in spatial econometric models when using micro-geographic data with location errors, enhancing the reliability of inferences in such contexts.

Contribution

It introduces a novel bias reduction strategy for spatial econometric models affected by locational errors in micro-geographic data.

Findings

Method effectively reduces bias in parameter estimates.

Simulation results confirm improved inference reliability.

Approach enables use of spatial models with imperfect location data.

Abstract

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.