LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises

DOI10.1080/01621459.2014.977386zbMath1373.62440OpenAlexW1969001826MaRDI QIDQ5367401

Shiqing Ling, Ke Zhu

Publication date: 13 October 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://repository.ust.hk/ir/bitstream/1783.1-71283/1/641912-Article-12-fulltext.pdf

zbMATH Keywords

asymptotic normalitystatistical inferencestrong consistencyself-weighted LADEsign-based portmanteau testLADEARMA\((p,q)\) modelsauto-regressive moving average (ARMA)G/ARCH noisesheavy-tailed noisesleast absolute deviation estimator (LADE)random weighting approach

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09)

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