An overview of bootstrap methods for estimating and predicting in time series

DOI10.1007/BF02595864zbMath0945.62048WikidataQ61849351 ScholiaQ61849351MaRDI QIDQ1302062

Ricardo Cao

Publication date: 3 October 2000

Published in: Test (Search for Journal in Brave)

zbMATH Keywords

autoregressive processesresampling methodsblockwise bootstrapstationary bootstrapmoving average processesmoving blocks bootstrap

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric statistical resampling methods (62G09)

Related Items (9)

Plug-in prediction intervals for a special class of standard ARH(1) processes ⋮ Bootstrap tests for nonparametric comparison of regression curves with dependent errors ⋮ Smoothed stationary bootstrap bandwidth selection for density estimation with dependent data ⋮ A Review and Some New Proposals for Bandwidth Selection in Nonparametric Density Estimation for Dependent Data ⋮ On robust forecasting in dynamic vector time series models ⋮ Functional methods for time series prediction: a nonparametric approach ⋮ Bandwidth selection for the local polynomial estimator under dependence: a simulation study ⋮ Bootstrap techniques in semiparametric estimation methods for ARFIMA models: A comparison study. ⋮ A nonparametric bootstrap method for spatial data

Cites Work

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• BOOTSTRAPPING STATIONARY AUTOREGRESSIVE MOVING‐AVERAGE MODELS
• On blocking rules for the bootstrap with dependent data
• Predicting Using Box-Jenkins, Nonparametric, and Bootstrap Techniques

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