Local Trigonometric Methods for Time Series Smoothing.

Gentile, Maria (2014) Local Trigonometric Methods for Time Series Smoothing. , [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Metodologia statistica per la ricerca scientifica, 26 Ciclo. DOI 10.6092/unibo/amsdottorato/6494.
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The thesis is concerned with local trigonometric regression methods. The aim was to develop a method for extraction of cyclical components in time series. The main results of the thesis are the following. First, a generalization of the filter proposed by Christiano and Fitzgerald is furnished for the smoothing of ARIMA(p,d,q) process. Second, a local trigonometric filter is built, with its statistical properties. Third, they are discussed the convergence properties of trigonometric estimators, and the problem of choosing the order of the model. A large scale simulation experiment has been designed in order to assess the performance of the proposed models and methods. The results show that local trigonometric regression may be a useful tool for periodic time series analysis.

Tipologia del documento
Tesi di dottorato
Gentile, Maria
Dottorato di ricerca
Scuola di dottorato
Scienze economiche e statistiche
Settore disciplinare
Settore concorsuale
Parole chiave
unobserved components , finite linear filters, ARIMA time series, frequency domain analysis, local trigonometric regression
Data di discussione
15 Maggio 2014

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