M-estimation for general ARMA Processes with Infinite Variance

DOI10.1002/sjos.12003zbMath1364.62229OpenAlexW1515271746MaRDI QIDQ2852629

Rongning Wu

Publication date: 9 October 2013

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sjos.12003

zbMATH Keywords

bootstraptime seriesinfinite varianceARMA processM-estimationstable distributionnon-causalitynon-invertibility

Mathematics Subject Classification ID

Infinitely divisible distributions; stable distributions (60E07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Non-Markovian processes: estimation (62M09) Nonparametric statistical resampling methods (62G09) Functional limit theorems; invariance principles (60F17)

Related Items (1)

Geometric ergodicity and conditional self‐weighted M‐estimator of a GRCAR(p) model with heavy‐tailed errors

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Rank-based estimation for all-pass time series models
• M-estimation for autoregression with infinite variance
• An approximate maximum likelihood estimation for non-Gaussian non-minimum phase moving average processes
• Gauss-Newton and M-estimation for ARMA processes with infinite variance
• Least absolute deviation estimation for all-pass time series models
• Parameter estimation for ARMA models with infinite variance innovations
• Gaussian and non-Gaussian linear time series and random fields
• Maximum likelihood estimation for \(\alpha \)-stable autoregressive processes
• Maximum likelihood estimation for all-pass time series models
• Consistent Estimation for Non-Gaussian Non-Causal Autoregessive Processes
• Quasi-likelihood Estimation of Non-invertible Moving Average Processes
• Least absolute deviation estimation for general ARMA time series models with infinite variance
• Least absolute deviation estimation for general autoregressive moving average time-series models
• Portmanteau tests for ARMA models with infinite variance
• TIME-REVERSIBILITY, IDENTIFIABILITY AND INDEPENDENCE OF INNOVATIONS FOR STATIONARY TIME SERIES

This page was built for publication: M-estimation for general ARMA Processes with Infinite Variance