TIME-REVERSIBILITY, IDENTIFIABILITY AND INDEPENDENCE OF INNOVATIONS FOR STATIONARY TIME SERIES

DOI10.1111/j.1467-9892.1992.tb00114.xzbMath0753.62058OpenAlexW2032060178MaRDI QIDQ4021564

Richard A. Davis, F. Jay Breidt

Publication date: 16 January 1993

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

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1992.tb00114.x

zbMATH Keywords

Gaussian processesfractional differencingARMA processesdeconvolution problemsnoncausaltime-reversibilityi.i.d. non-Gaussian noiseindependent noisecausal autoregressive moving-average modelscharacterizations of Gau Gaussian distributiongeneral linear processesinnovations sequencenon-Gaussian fractionally integrated ARMA processone-step prediction residuals

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Characterization and structure theory of statistical distributions (62E10)

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Cites Work

• Maximum standardized cumulant deconvolution of non-Gaussian linear processes
• Time series: theory and methods
• Estimation of models of autoregressive signal plus white noise
• Asymptotic behavior of least-squares estimates for autoregressive processes with infinite variances
• Almost sure convergence analysis of autoregressive spectral estimation in additive noise
• The uniqueness of moving average representations with independent and identically distributed random variables for non-Gaussian stationary time series
• On time-reversibility and the uniqueness of moving average representations for non-Gaussian stationary time series
• Linear processes and bispectra
• Time-reversibility of linear stochastic processes

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