Peer Groups and Bias Detection in Least Squares Regression

Blankmeyer, Eric (2021): Peer Groups and Bias Detection in Least Squares Regression.

Abstract

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 simulation

• Item Type: MPRA Paper
• Language: English
• Keywords: peer groups, least-squares bias, spatial autoregression
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics

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