Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables
AbstractThe 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. View Full-Text
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Griffith, D.A.; Chun, Y. Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables. Econometrics 2016, 4, 29.
Griffith DA, Chun Y. Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables. Econometrics. 2016; 4(2):29.Chicago/Turabian Style
Griffith, Daniel A.; Chun, Yongwan. 2016. "Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables." Econometrics 4, no. 2: 29.
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