Bootstrap prediction intervals for autoregressive time series

DOI10.1016/J.CSDA.2006.09.012zbMath1161.62406OpenAlexW2169620660MaRDI QIDQ1019991

J. Martínez

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.09.012

zbMATH Keywords

autoregressive modelsbias correctionbootstrapping

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09)

Related Items (13)

Polarization of forecast densities: a new approach to time series classification ⋮ Bootstrap prediction bands for forecast paths from vector autoregressive models ⋮ Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models ⋮ A note on simultaneous calibrated prediction intervals for time series ⋮ Prediction intervals in the beta autoregressive moving average model ⋮ Simulated minimum Hellinger distance estimation of stochastic volatility models ⋮ Model-based INAR bootstrap for forecasting INAR\((p)\) models ⋮ Bias-corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting ⋮ Bias correction for time series factor models ⋮ Computational techniques for applied econometric analysis of macroeconomic and financial processes ⋮ A time series bootstrap procedure for interpolation intervals ⋮ Approximate regenerative-block bootstrap for Markov chains ⋮ Improved bootstrap prediction intervals for SETAR models

Uses Software

• bootstrap
• bootlib

Cites Work

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• Recent developments in bootstrapping time series
• Bootstrapping time series models
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• Bootstrapping Autoregressive Processes with Possible Unit Roots
• The bootstrap and Edgeworth expansion
• Forecasting time series with sieve bootstrap
• A new look at the statistical model identification

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