A correlation between regressors and disturbances presents challenging problems in linear regression. In the context of spatial econometrics LeSage and Pace (2009) show that an autoregressive model estimated by maximum likelihood may be able to detect least squares bias. I suggest that spatial neighbors can be replaced by “peer groups” as in Blankmeyer et al. (2011), thereby extending considerably the range of contexts where the autoregressive model can be utilized. The procedure is applied to two data sets and in a simulatio
