Improved prediction intervals for stochastic process models

DOI10.1111/j.1467-9892.2004.00341.xzbMath1051.62094OpenAlexW3125073828MaRDI QIDQ4828174

Paolo Vidoni

Publication date: 24 November 2004

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2004.00341.x

zbMATH Keywords

predictionconditional approachARCH modelAR modelparametric statistical model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20)

Related Items (12)

A note on simultaneous calibrated prediction intervals for time series ⋮ The improved value-at-risk for heteroscedastic processes and their coverage probability ⋮ Improved multivariate prediction regions for Markov process models ⋮ The Relative Efficiency of Prediction Intervals ⋮ Response prediction in mixed effects models ⋮ The asymptotic efficiency of improved prediction intervals ⋮ Improved Prediction Limits For AR(p) and ARCH(p) Processes ⋮ A justification of conditional confidence intervals ⋮ A simple procedure for computing improved prediction intervals for autoregressive models ⋮ Improved prediction limits for a general class of Gaussian models ⋮ Quantile-based estimative VaR forecast and dependence measure: a simulation approach ⋮ Methods to compute prediction intervals: a review and new results

Cites Work

• Unnamed Item
• The sampling distribution of forecasts from a first-order autoregression
• Asymptotic theory of statistical inference for time series
• Biometrika Centenary: Nonparametrics
• The Relevance Property For Prediction Intervals
• The bootstrap and Edgeworth expansion

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