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Quantile spectral analysis and long-memory time series

Published online by Cambridge University Press:  14 July 2016

Emanuel Parzen
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Abstract

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.

Keywords

MODEL IDENTIFICATIONQUANTILE DATA ANALYSISHURST EXPONENTSPECTRAL INDEX OF REGULAR VARIATION

Information

Type
Part 1—Structure and General Methods for Time Series
Information
Journal of Applied Probability , Volume 23 , Issue A: Essays in Time Series and Allied Processes , 1986, pp. 41 - 54
Copyright
Copyright © 1986 Applied Probability Trust 

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