Weiß, Christian H.Ruiz Marín, ManuelKeller, KarstenMatilla García, Mariano2024-12-022024-12-022022-04Weiß, C. H., Marín, M. R., Keller, K., & Matilla-García, M. (2022). Non-parametric analysis of serial dependence in time series using ordinal patterns. Computational Statistics & Data Analysis, 168, 107381. https://doi.org/10.1016/j.csda.2021.1073810167-9473https://doi.org/10.1016/j.csda.2021.107381https://hdl.handle.net/20.500.14468/24622Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en Computational Statistics & Data Analysis, (2022) 168, 107381, está disponible en línea en el sitio web del editor: https://doi.org/10.1016/j.csda.2021.107381 This is the accepted manuscript of the article. The registered version was first published in Computational Statistics & Data Analysis, (2022) 168, 107381, it is available online at the publisher's website: https://doi.org/10.1016/j.csda.2021.107381A list of new tests for serial dependence based on ordinal patterns are provided. These new methods rely exclusively on the order structure of the data sets. Hence, the novel tests are stable under monotone transformations of the time series and robust against small perturbations or measurement errors. The standard asymptotic distributions are given, and their finite sample behavior under linear and non-linear departures from the null of independence are studied. Moreover, it is proved that under mild conditions, any ordinal-pattern-based test is nuisance free, which is appealing for modelling, as these tests can eventually be used as misspecification tests. This property is also analyzed for finite samples and illustrated through an empirical application. Much of the discussion is based on a detailed combinatorial analysis of ordinal pattern probabilitieseninfo:eu-repo/semantics/openAccess53 Ciencias Económicasartículonon-parametric testsordinal patternsordinal time seriesreal-valued time seriesserial dependence