Nonlinear log-periodogram regression for perturbed fractional processes

DOI10.1016/S0304-4076(03)00115-5zbMath1027.62067OpenAlexW2096486132MaRDI QIDQ1398966

Yixiao Sun, Peter C. B. Phillips

Publication date: 7 August 2003

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4076(03)00115-5

zbMATH Keywords

Asymptotic biasBias reductionAsymptotic normalityRate of convergenceFractional components modelPerturbed fractional processTesting perturbations

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Inference from stochastic processes and spectral analysis (62M15)

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Cites Work

• The detection and estimation of long memory in stochastic volatility
• Higher-order kernel semiparametric M-estimation of long memory
• Log-periodogram regression of time series with long range dependence
• Gaussian semiparametric estimation of long range dependence
• Exact local Whittle estimation of fractional integration
• THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
• Discrimination between monotonic trends and long-range dependence
• The mean squared error of Geweke and Porter-Hudak's estimator of the memory parameter of a long-memory time series
• ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS
• Plug‐in Selection of the Number of Frequencies in Regression Estimates of the Memory Parameter of a Long‐memory Time Series
• NON-GAUSSIAN LOG-PERIODOGRAM REGRESSION
• A Bias-Reduced Log-Periodogram Regression Estimator for the Long-Memory Parameter

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