Accession Number : ADA132240

Title :   Time Series Long Memory Identification and Quantile Spectral Analysis.

Descriptive Note : Technical rept.,

Corporate Author : TEXAS A AND M UNIV COLLEGE STATION DEPT OF STATISTICS

Personal Author(s) : Parzen,Emanuel

PDF Url : ADA132240

Report Date : Aug 1983

Pagination or Media Count : 37

Abstract : An approach to spectral estimation is described which involves the simultaneous use of frequency, time, and quantile domain algorithms, and is called quantile spectral analysis. It is based on the premise that while the spectrum is a non-parametric concept, its estimation cannot be a non-parametric procedure to be conducted independently of model identification. We discuss: the goals of spectral analysis, quantile data analysis, identification of memory (no, short, long), index of regular variation of a spectral density, autoregressive spectral estimation, and ARMA model identification by estimating MA (infinity) and subset regression. An illustrative example is given of quantile spectral analysis. (Author)

Descriptors :   Statistical analysis, *Spectrum analysis, *Estimates, *Time series analysis, Identification, Parametric analysis, Correlation techniques, Nonparametric statistics, Mathematical models, Computations, Estimates

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE