Publication: Asymptotic distribution-free tests for semiparametric regressions with dependent data
dc.affiliation.dpto | UC3M. Departamento de EconomÃa | es |
dc.contributor.author | Escanciano, Juan Carlos | |
dc.contributor.author | Pardo-Fernandez, Juan Carlos | |
dc.contributor.author | Van Keilegom, Ingrid | |
dc.contributor.funder | Ministerio de EconomÃa y Competitividad (España) | es |
dc.contributor.funder | European Commission | en |
dc.date.accessioned | 2022-06-13T15:01:23Z | |
dc.date.available | 2022-06-13T15:01:23Z | |
dc.date.issued | 2018-06-01 | |
dc.description.abstract | This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly dependent data coming from a strictly stationary process. Classical tests based on the difference between the estimated distributions of the restricted and unrestricted regression errors are not ADF. In this article, we introduce a novel transformation of this difference that leads to ADF tests with well-known critical values. The general methodology is illustrated with applications to testing for parametric models against nonparametric or semiparametric alternatives, and semiparametric constrained mean-variance models. Several Monte Carlo studies and an empirical application show that the finite sample performance of the proposed tests is satisfactory in moderate sample sizes. | en |
dc.description.sponsorship | Supported by the Spanish Plan Nacional de I+D+I, reference number ECO2014-55858-P. Supported by the SpanishMinisterio de EconomÃa y Competitividad (GrantMTM2014-55966 P). Supported by the European Research Council (2016-2021, Horizon 2020 ERC Grant agreement Nº. 694409), and from IAP Research Network P7/06 of the Belgian State | es |
dc.identifier.bibliographicCitation | Escanciano, J. C., Pardo-Fernández, J. C., & Van Keilegom, I. (2018). Asymptotic distribution-free tests for semiparametric regressions with dependent data.The Annals of Statistics, 46 (3), pp. 1167 - 1196 | en |
dc.identifier.doi | https://doi.org/10.1214/17-AOS1581 | |
dc.identifier.issn | 0090-5364 | |
dc.identifier.publicationfirstpage | 1167 | es |
dc.identifier.publicationissue | 3 | es |
dc.identifier.publicationlastpage | 1196 | es |
dc.identifier.publicationtitle | ANNALS OF STATISTICS | en |
dc.identifier.publicationvolume | 43 | es |
dc.identifier.uri | https://hdl.handle.net/10016/35090 | |
dc.identifier.uxxi | AR/0000029575 | |
dc.language.iso | eng | es |
dc.publisher | Institute of Mathematical Statistics | en |
dc.relation.projectID | Gobierno de España. ECO2014-55858-P | es |
dc.relation.projectID | Gobierno de España. MTM2014-55966-P | es |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/694409 | es |
dc.rights | © 2018 Institute of Mathematical Statistics | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | EconomÃa | es |
dc.subject.other | Beta-mixing | en |
dc.subject.other | Goodness-of-fit tests | en |
dc.subject.other | Local polynomial estimation | en |
dc.subject.other | Nonparametric regression | en |
dc.subject.other | Error distribution | en |
dc.title | Asymptotic distribution-free tests for semiparametric regressions with dependent data | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |