A log log law for unstable ARMA models with applications to time series analysis
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Publication:1185829
DOI10.1016/0047-259X(92)90021-7zbMath0746.62093MaRDI QIDQ1185829
Publication date: 28 June 1992
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
time seriesmartingaleswhite noiselog log lawautocorrelations of unstable ARMA modelsbackshift operatorKolmogorov's law of iterated logarithmrates of convergence of estimatorsunstable ARMA processes
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Strong limit theorems (60F15)
Related Items (3)
On asymptotic properties of bootstrap for AR(1) processes ⋮ Consistency of the maximum likelihood estimators for nonstationary ARMA regressions with time trends ⋮ Asymptotic canonical forms and iterated logarithm rate results of least squares estimates for unstable ARMA models
Cites Work
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- Asymptotic properties of general autoregressive models and strong consistency of least-squares estimates of their parameters
- SELECTING ORDER FOR GENERAL AUTOREGRESSIVE MODELS BY MINIMUM DESCRIPTION LENGTH
- Orthonormal Banach systems with applications to linear processes
- Local Convergence of Martingales and the Law of Large Numbers
- On Limit Theorems for Quadratic Functions of Discrete Time Series
- An iterated logarithm result for martingales and its application in estimation theory for autoregressive processes
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