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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4927
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dc.contributor.authorMcElroy, Tucker-
dc.contributor.authorWildi, Marc-
dc.date.accessioned2018-12-07T09:21:22Z-
dc.date.available2018-12-07T09:21:22Z-
dc.date.issued2010-
dc.identifier.issn2194-6507de_CH
dc.identifier.issn1941-1928de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13642-
dc.descriptionerworben im Rahmen der Schweizer Nationallizenzen (www.nationallizenzen.ch)de_CH
dc.description.abstractTypically, model misspecification is addressed by statistics relying on model-residuals, i.e., on one-step ahead forecasting errors. In practice, however, users are often also interested in problems involving multi-step ahead forecasting performances, which are not explicitly addressed by traditional diagnostics. In this article, we consider the topic of misspecification from the perspective of signal extraction. More precisely, we emphasize the connection between models and real-time (concurrent) filter performances by analyzing revision errors instead of one-step ahead forecasting errors. In applications, real-time filters are important for computing trends, for performing seasonal adjustment or for inferring turning-points towards the current boundary of time series. Since revision errors of real-time filters generally rely on particular linear combinations of one- and multi-step ahead forecasts, we here address a generalization of traditional diagnostics. Formally, a hypothesis testing paradigm for the empirical revision measure is developed through theoretical calculations of the asymptotic distribution under the null hypothesis, and the method is assessed through real data studies as well as simulations. In particular, we analyze the effect of model misspecification with respect to unit roots, which are likely to determine multi-step ahead forecasting performances. We also show that this framework can be extended to general forecasting problems by defining suitable artificial signals.de_CH
dc.language.isoende_CH
dc.publisherDe Gruyterde_CH
dc.relation.ispartofJournal of Time Series Econometricsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectReal-time filteringde_CH
dc.subjectSignal extractionde_CH
dc.subjectModel-diagnosticsde_CH
dc.subjectNonstationary time seriesde_CH
dc.subjectSeasonalityde_CH
dc.subject.ddc003: Systemede_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleSignal extraction revision variances as a goodness-of-fit measurede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.21256/zhaw-4927-
dc.identifier.doi10.2202/1941-1928.1012de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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McElroy, T., & Wildi, M. (2010). Signal extraction revision variances as a goodness-of-fit measure. Journal of Time Series Econometrics, 2(1). https://doi.org/10.21256/zhaw-4927
McElroy, T. and Wildi, M. (2010) ‘Signal extraction revision variances as a goodness-of-fit measure’, Journal of Time Series Econometrics, 2(1). Available at: https://doi.org/10.21256/zhaw-4927.
T. McElroy and M. Wildi, “Signal extraction revision variances as a goodness-of-fit measure,” Journal of Time Series Econometrics, vol. 2, no. 1, 2010, doi: 10.21256/zhaw-4927.
MCELROY, Tucker und Marc WILDI, 2010. Signal extraction revision variances as a goodness-of-fit measure. Journal of Time Series Econometrics. 2010. Bd. 2, Nr. 1. DOI 10.21256/zhaw-4927
McElroy, Tucker, and Marc Wildi. 2010. “Signal Extraction Revision Variances as a Goodness-of-Fit Measure.” Journal of Time Series Econometrics 2 (1). https://doi.org/10.21256/zhaw-4927.
McElroy, Tucker, and Marc Wildi. “Signal Extraction Revision Variances as a Goodness-of-Fit Measure.” Journal of Time Series Econometrics, vol. 2, no. 1, 2010, https://doi.org/10.21256/zhaw-4927.


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