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Published in:
2011 | OriginalPaper | Chapter
14. Learning from Residuals
Authors : Prof. Daniel A. Griffith, Prof. Dr. Jean H.P. Paelinck
Published in: Non-standard Spatial Statistics and Spatial Econometrics
Publisher: Springer Berlin Heidelberg
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Abstract
Residuals often are considered as a troublesome noise in spatial—or, for that matter—non-spatial econometric models. Current practice in spatial econometrics is to set up a spatial error model, more often than not with an exogenous W spatial weight matrix, in order to improve the efficiency of the estimators.
Looking closely into the residuals is less common practice. And still, residuals can represent extremely precious building blocks for further work, as other disciplines have shown. Around 1850 the British chemists, Mansfield and Perkin, had the—for that era of chemistry—strange idea to analyze the composition of tar, until then exclusively used to improve coverage of roads (John London McAdam had his name attached to that technique, tarmacadam); the result of the British chemists’ investigation was the roaring development of a whole branch of (industrial) chemistry: carbochemistry.