Bootstrap techniques in semiparametric estimation methods for ARFIMA models: A comparison study.

DOI10.1007/BF02892059zbMath1062.62171OpenAlexW2081283631MaRDI QIDQ1775955

Glaura C. Franco, Valdério Anselmo Reisen

Publication date: 20 May 2005

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02892059

zbMATH Keywords

resampling methodsbootstrap methodslocal bootstrapfractionally integrated ARMA processesregression bootstrap

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09) Monte Carlo methods (65C05)

Related Items (7)

Reliability in portfolio optimization using uncertain estimates ⋮ Using the bootstrap for finite sample confidence intervals of the log periodogram regression ⋮ Bootstrap-based bandwidth choice for log-periodogram regression ⋮ Local bootstrap approaches for fractional differential parameter estimation in ARFIMA models ⋮ Correlated Errors in the Parameters Estimation of the ARFIMA Model: A Simulated Study ⋮ Unit root tests using semi-parametric estimators of the long-memory parameter ⋮ Bootstrap testing for discontinuities under long-range dependence

Uses Software

• bootstrap
• bootlib

Cites Work

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• A comparison of techniques of estimation in long-memory processes.
• Local bootstrap
• Bootstrap methods: another look at the jackknife
• An overview of bootstrap methods for estimating and predicting in time series
• Semiparametric analysis of long-memory time series
• The jackknife and the bootstrap for general stationary observations
• Log-periodogram regression of time series with long range dependence
• Averaged periodogram estimation of long memory
• THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter
• ESTIMATION OF THE FRACTIONAL DIFFERENCE PARAMETER IN THE ARIMA(p, d, q) MODEL USING THE SMOOTHED PERIODOGRAM
• The Stationary Bootstrap
• Nonparametric testing of closeness between two unknown distribution functions
• Comparing the bias and misspecification in ARFIMA models
• ESTIMATION OF PARAMETERS IN ARFIMA PROCESSES: A SIMULATION STUDY
• The Local Bootstrap for Periodogram Statistics
• Parametric and semiparametric estimations of stationary univariate ARFIMA models

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