Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables

Daniel A. Griffith and Yongwan Chun
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Daniel A. Griffith: School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Yongwan Chun: School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA

Econometrics, 2016, vol. 4, issue 2, 1-12

Abstract: The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification ( i.e. , the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional covariates in a second regression analysis. The former regression model can be considered restricted, whereas the latter model can be considered unrestricted; this first model is nested within this second model. A RESET significance test is conducted with an F -test using the error sums of squares and the degrees of freedom for the two models. For georeferenced data, eigenvectors can be extracted from a modified spatial weights matrix, and included in a linear regression model specification to account for the presence of nonzero spatial autocorrelation. The intuition underlying this methodology is that these synthetic variates function as surrogates for omitted variables. Accordingly, a restricted regression model without eigenvectors should indicate an omitted variables problem, whereas an unrestricted regression model with eigenvectors should result in a failure to reject the RESET null hypothesis. This paper furnishes eleven empirical examples, covering a wide range of spatial attribute data types, that illustrate the effectiveness of eigenvector spatial filtering in addressing the omitted variables problem for georeferenced data as measured by the RESET.

Keywords: eigenvector spatial filter; omitted variables; RESET; spatial autocorrelation; specification error (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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