Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Journal of time series econometrics, 2017-01, Vol.9 (1), p.593
2017

Details

Autor(en) / Beteiligte
Titel
Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules
Ist Teil von
  • Journal of time series econometrics, 2017-01, Vol.9 (1), p.593
Ort / Verlag
Berlin: De Gruyter
Erscheinungsjahr
2017
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • This paper presents a flexible framework for signal extraction of time series measured as stock or flow at diverse sampling frequencies. Our approach allows for a coherent treatment of series across diverse sampling rules, a deeper understanding of the main properties of signal estimators and the role of measurement, and a straightforward method for signal estimation and interpolation for discrete observations. We set out the essential theoretical foundations, including a proof of the continuous-time Wiener-Kolmogorov formula generalized to nonstationary signal or noise. Based on these results, we derive a new class of low-pass filters that provide the basis for trend estimation of stock and flow time series. Further, we introduce a simple and accurate method for low-frequency signal estimation and interpolation in discrete samples, and examine its properties for simulated series. Illustrations are given on economic data.

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX