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Article: Functional coefficient regression models for non-linear time series: A polynomial spline approach

TitleFunctional coefficient regression models for non-linear time series: A polynomial spline approach
Authors
KeywordsVarying coefficient model
Threshold autoregressive model
Forecasting
Functional autoregressive model
Non-parametric regression
Issue Date2004
Citation
Scandinavian Journal of Statistics, 2004, v. 31, n. 4, p. 515-534 How to Cite?
AbstractWe propose a global smoothing method based on polynomial splines for the estimation of functional coefficient regression models for non-linear time series. Consistency and rate of convergence results are given to support the proposed estimation method. Methods for automatic selection of the threshold variable and significant variables (or lags) are discussed. The estimated model is used to produce multi-step-ahead forecasts, including interval forecasts and density forecasts. The methodology is illustrated by simulations and two real data examples.
Persistent Identifierhttp://hdl.handle.net/10722/219478
ISSN
2015 Impact Factor: 0.908
2015 SCImago Journal Rankings: 1.362

 

DC FieldValueLanguage
dc.contributor.authorHuang, Jianhua Z.-
dc.contributor.authorShen, Haipeng-
dc.date.accessioned2015-09-23T02:57:11Z-
dc.date.available2015-09-23T02:57:11Z-
dc.date.issued2004-
dc.identifier.citationScandinavian Journal of Statistics, 2004, v. 31, n. 4, p. 515-534-
dc.identifier.issn0303-6898-
dc.identifier.urihttp://hdl.handle.net/10722/219478-
dc.description.abstractWe propose a global smoothing method based on polynomial splines for the estimation of functional coefficient regression models for non-linear time series. Consistency and rate of convergence results are given to support the proposed estimation method. Methods for automatic selection of the threshold variable and significant variables (or lags) are discussed. The estimated model is used to produce multi-step-ahead forecasts, including interval forecasts and density forecasts. The methodology is illustrated by simulations and two real data examples.-
dc.languageeng-
dc.relation.ispartofScandinavian Journal of Statistics-
dc.subjectVarying coefficient model-
dc.subjectThreshold autoregressive model-
dc.subjectForecasting-
dc.subjectFunctional autoregressive model-
dc.subjectNon-parametric regression-
dc.titleFunctional coefficient regression models for non-linear time series: A polynomial spline approach-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-10444247376-
dc.identifier.volume31-
dc.identifier.issue4-
dc.identifier.spage515-
dc.identifier.epage534-

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