Subsampling the mean of heavy‐tailed dependent observations

DOI10.1046/j.0143-9782.2003.00346.xzbMath1051.62078OpenAlexW3124170919MaRDI QIDQ4828178

Michael Wolf, Piotr S. Kokoszka

Publication date: 24 November 2004

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

Full work available at URL: http://hdl.handle.net/10230/384

zbMATH Keywords

heavy tailssubsamplingestimation of meanGARCH processes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (11)

The spurious regression of AR(\(p\)) infinite-variance sequence in the presence of structural breaks ⋮ Ratio detections for change point in heavy tailed observations ⋮ Monitoring persistent change in a heavy-tailed sequence with polynomial trends ⋮ Detection and estimation of structural change in heavy-tailed sequence ⋮ Bootstrap testing multiple changes in persistence for a heavy-tailed sequence ⋮ Bootstrap Testing for Changes in Persistence with Heavy-Tailed Innovations ⋮ Truncating Estimation for the Mean Change-Point in Heavy-Tailed Dependent Observations ⋮ Bootstrap tests for structural change with infinite variance observations ⋮ Monitoring persistence change in infinite variance observations ⋮ Subsampling tests for the mean change point with heavy-tailed innovations ⋮ Monitoring Change in Persistence Against the Null of Difference-Stationarity in Infinite Variance Observations

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linear prediction of ARMA processes with infinite variance
• Time series: theory and methods.
• Mixing: Properties and examples
• Parameter estimation for infinite variance fractional ARIMA
• Regular variation of GARCH processes.
• The sample autocorrelations of heavy-tailed processes with applications to ARCH
• A characterization of multivariate regular variation.
• Can One Use the Durbin–Levinson Algorithm to Generate Infinite Variance Fractional ARIMA Time Series?
• MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
• ROBUST INFERENCE FOR THE MEAN IN THE PRESENCE OF SERIAL CORRELATION AND HEAVY-TAILED DISTRIBUTIONS

This page was built for publication: Subsampling the mean of heavy‐tailed dependent observations