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Structural changes estimation for strongly dependent processes. (English) Zbl 1453.62644

Summary: In this paper, we consider the problem of estimating multiple structural breaks in a long-memory fractional autoregressive integrated moving-average time series. The number of break points as well as their locations, the orders and the parameters of each regime are assumed to be unknown. A selection criterion based on the minimum description length principle is proposed and a genetic algorithm is implemented for its optimization. Monte Carlo simulation results show the effectiveness of this criterion and an application to the Nile River data is considered.

MSC:

62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
60G35 Signal detection and filtering (aspects of stochastic processes)
90C59 Approximation methods and heuristics in mathematical programming

Software:

LASS; longmemo
PDFBibTeX XMLCite
Full Text: DOI

References:

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