Meilán-Vila, AndreaOpsomer, JeanFrancisco-Fernández, MarioCrujeiras-Casais, Rosa M.2023-11-242023-11-242020Meilán-Vila, A., Opsomer, J.D., Francisco-Fernández, M. et al. A goodness-of-fit test for regression models with spatially correlated errors. TEST 29, 728–749 (2020). https://doi.org/10.1007/s11749-019-00678-yhttp://hdl.handle.net/2183/34328Versión final aceptada de: https://doi.org/10.1007/s11749-019-00678-yThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11749-019-00678-yThe 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 -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.engTodos os dereitos reservados. All rights reserved.Model checkingSpatial correlationLocal linear regressionLeast squaresBootstrapjournal articleopen access10.1007/s11749-019-00678-y