{"data":{"id":"10.6084/m9.figshare.c.2092748.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2092748.v1","prefix":"10.6084","suffix":"m9.figshare.c.2092748.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Zhou, Zhou","givenName":"Zhou","familyName":"Zhou","affiliation":[],"nameIdentifiers":[]},{"name":"Wu, Weichi","givenName":"Weichi","familyName":"Wu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Nonparametric Inference for Time-varying Coefficient Quantile Regression"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences"},{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Genetics"},{"subject":"Medicine"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2019-08-19","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.c.2092748","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The paper considers nonparametric inference for quantile regression models with time-varying coefficients. The errors and covariates of the regression are assumed to belong to a general class of locally stationary processes and are allowed to be cross-dependent. Simultaneous confidence tubes (SCT) and integrated squared difference tests (ISDT) are proposed for simultaneous nonparametric inference of the latter models with asymptotically correct coverage probabilities and type I error rates. Our methodologies are shown to possess certain asymptotically optimal properties. Furthermore, we propose an information criterion which performs consistent model selection for nonparametric quantile regression models of non-stationary time series. For implementation, a wild bootstrap procedure is proposed which is shown to be robust to the dependent and non-stationary data structure. Our method is applied to studying the asymmetric and time-varying dynamic structures of the US unemployment rate since the 1940s.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/collections/Nonparametric_Inference_for_Time_varying_Coefficient_Quantile_Regression/2092748/1","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-12-05T20:29:29.000Z","registered":"2015-12-05T20:29:30.000Z","published":"2015","updated":"2024-03-21T09:20:19.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.c.2092748.v1","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}