STRONG CONSISTENCY AND ASYMPTOTIC NORMALITY OF /1 ESTIMATES OF THE AUTOREGRESSIVE MOVING-AVERAGE MODEL

DOI10.1111/j.1467-9892.1991.tb00071.xzbMath0712.62085OpenAlexW2007348294MaRDI QIDQ3197164

William T. M. Dunsmuir, N. M. Spencer

Publication date: 1991

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

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1991.tb00071.x

zbMATH Keywords

asymptotic normalityinnovationscentral limit theoremARMAStrong consistencyleast absolute deviation estimatesl1 estimationscalar autoregressive- moving average model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05)

Related Items (8)

Least absolute deviation estimation of stationary time series models ⋮ THE GLOBAL WEIGHTED LAD ESTIMATORS FOR FINITE/INFINITE VARIANCE ARMA(p,q) MODELS ⋮ Bahadur-Kiefer representations for GM-estimators in autoregression models ⋮ The consistency of the L₁norm estimates in arma models ⋮ Comparison of efficiency of estimates by the methods of least absolute deviations and least squares in the autoregression model with random coefficient ⋮ Least absolute value regression: recent contributions ⋮ Consistency of global LSE for MA(1) models ⋮ Robust goodness-of-fit tests for \(\text{AR} (p)\) models based on \(L_1\)-norm fitting

Uses Software

• MINOS

Cites Work

• Asymptotic optimal inference for non-ergodic models
• The Lindeberg-Levy Theorem for Martingales
• Unnamed Item

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