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1997-07-01Buch DOI: 10.18452/3836
Nonparametric Lag Selection for Time Series
dc.contributor.authorTschernig, Rolf
dc.contributor.authorYang, Lijian
dc.date.accessioned2017-06-15T22:17:53Z
dc.date.available2017-06-15T22:17:53Z
dc.date.created2006-06-07
dc.date.issued1997-07-01
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4488
dc.description.abstractA nonparametric version of the Final Prediction Error (FPE) is proposed for lag selection in nonlinear autoregressive time series. We derive its consistency for both local constant and local linear estimators using a derived optimal bandwidth. Further asymptotic analysis suggests a greater probability of overfitting (too many lags) than underfitting (missing important lags). Thus a correction factor is proposed to increase correct fitting by reducing overfitting. Our Monte-Carlo study also corroborates that the correction factor generally improves the probability of correct lag selection for both linear and nonlinear processes. The proposed methods are successfully applied to the Canadian lynx data and daily returns of DM/US-Dollar exchange rates.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectConsistencyeng
dc.subjectFinal Prediction Erroreng
dc.subjectForeign Exchange Rateseng
dc.subjectLag Selectioneng
dc.subjectNonlinear Autoregressioneng
dc.subjectNonparametric Methodeng
dc.subject.ddc330 Wirtschaft
dc.titleNonparametric Lag Selection for Time Series
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10064444
dc.identifier.doihttp://dx.doi.org/10.18452/3836
dc.subject.dnb17 Wirtschaft
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages30
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume1997
local.edoc.container-issue59
local.edoc.container-year1997
local.edoc.container-erstkatid2135319-0

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