Piecewise Farima Models for Long-memory Time Series
نویسندگان
چکیده
We consider the problem of modeling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the break points and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modeling is considered.
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