Prediction intervals for farima processes by bootstrap methods

DOI10.1080/00949650108812065zbMath0996.62087OpenAlexW2077923359MaRDI QIDQ2708090

Matteo Grigoletto, Luisa Bisaglia

Publication date: 5 November 2002

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650108812065

zbMATH Keywords

bootstraplong-range dependenceprediction intervalsWhittle estimatorlong-range forecastingfractional ARIMA processes

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09)

Related Items (1)

Obtaining prediction intervals for FARIMA processes using the sieve bootstrap

Cites Work

• Efficient parameter estimation for self-similar processes
• Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
• A comparison of techniques of estimation in long-memory processes.
• Time series: theory and methods.
• Bootstrap methods: another look at the jackknife
• A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotical normality of Whittle's estimate
• THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
• Bootstrap Prediction Intervals for Autoregression
• Bootstrap confidence intervals for a class of parametric problems
• A Test of Goodness of Fit
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