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Titel: Optimal real-time filters for linear prediction problems
Autor/-in: Wildi, Marc
McElroy, Tucker
Erschienen in: Journal of time series econometrics
Band(Heft): 8
Heft: 2
Verlag / Hrsg. Institution: De Gruyter
Erscheinungsdatum: 2016
Lizenz (gemäss Verlagsvertrag): CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Art der Begutachtung: Peer review (Publikation)
Sprache: Englisch
Fachgebiet (DDC): 003: Systeme
Zusammenfassung: The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e. g., forecasting or signal extraction) – or features driven by the priorities of the data-users. A single optimization principle (one-step ahead forecast error minimization) is proposed by this classical paradigm to address a plethora of prediction problems. In contrast, this paper proposes to reconcile prediction problem structures, user priorities, and optimization principles into a general framework whose scope encompasses the classic approach. We introduce the linear prediction problem (LPP), which in turn yields an LPP objective function. Then one can fit models via LPP minimization, or one can directly optimize the linear filter corresponding to the LPP, yielding the Direct Filter Approach. We provide theoretical results and practical algorithms for both applications of the LPP, and discuss the merits and limitations of each. Our empirical illustrations focus on trend estimation (low-pass filtering) and seasonal adjustment in real-time, i. e., constructing filters that depend only on present and past data.
Departement: School of Engineering
Organisationseinheit: Institut für Datenanalyse und Prozessdesign (IDP)
Publikationstyp: Beitrag in wissenschaftlicher Zeitschrift
DOI: 10.21256/zhaw-4906
ISSN: 2194-6507
Enthalten in den Sammlungen:Publikationen School of Engineering

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