Measurement errors in a spatial context

Measurement error in an independent variable is one reason why OLS estimates may not be consistent. However, as shown by Dagenais (1994), in some circumstances the OLS bias may be ameliorated somewhat given the presence of serially correlated disturbances, and OLS may prove superior to standard techniques used to correct for serial correlation. This paper considers the case of cross-sectional regression models with measurement errors in the explanatory variables and with spatial dependence. The study focuses on the evidence provided by an empirical illustration and Monte Carlo experiments examining measurement error impact in the presence of autoregressive error processes and autoregressive spatial lags.

► We analyze the effects of measurement error in a spatial context. ► We compare various estimators, which do and do not control for spatial error terms. ► We present an empirical illustration and perform Monte-Carlo simulations. ► OLS and instrumental variables outperform GMM and ML based estimators.