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Peer Groups and Bias Detection in Least Squares Regression

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  • Blankmeyer, Eric

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

Suggested Citation

  • Blankmeyer, Eric, 2021. "Peer Groups and Bias Detection in Least Squares Regression," MPRA Paper 110866, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110866
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    More about this item

    Keywords

    peer groups; least-squares bias; spatial autoregression;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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