A goodness-of-fit test for regression models with spatially correlated errors

Andrea Meilán-Vila (), Jean D. Opsomer, Mario Francisco-Fernández and Rosa M. Crujeiras
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Andrea Meilán-Vila: Universidade da Coruña
Jean D. Opsomer: Westat
Mario Francisco-Fernández: Universidade da Coruña
Rosa M. Crujeiras: Universidade de Santiago de Compostela

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 3, No 6, 728-749

Abstract: Abstract The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a $$L_2$$ L 2 -distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a bootstrap procedure is designed to calibrate the test in practice. Finite sample performance of the test is analyzed through a simulation study, and its applicability is illustrated using a real data example.

Keywords: Model checking; Spatial correlation; Local linear regression; Least squares; Bootstrap; 62G10; 62H11; 62G08; 62G09 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-019-00678-y

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