Polynomial Trend Regression With Long‐memory Errors

DOI10.1111/j.1467-9892.2005.00405.xzbMath1092.62086OpenAlexW3121274040MaRDI QIDQ5467606

Hwai-Chung Ho, Nan-Jung Hsu

Publication date: 24 May 2006

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

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2005.00405.x

zbMATH Keywords

ordinary least squares

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10) Inference from stochastic processes and spectral analysis (62M15)

Related Items

Asymptotic inference in some heteroscedastic regression models with long memory design and errors, Lack of fit test for long memory regression models

Uses Software

• longmemo

Cites Work

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• Time series: theory and methods
• Asymptotic normality of regression estimators with long memory errors
• The central limit theorem for time series regression
• Multiple Wiener-Ito integrals. With applications to limit theorems
• M-estimators in linear models with long range dependent errors
• Asymptotics of R-, MD- and LAD-estimators in linear regression models with long range dependent errors
• Time series regression with long-range dependence
• Asymptotic expansion of \(M\)-estimators with long-memory errors
• Convergence of integrated processes of arbitrary Hermite rank
• Linear Trend with Fractionally Integrated Errors
• Asymptotic theory for certain regression models with long memory errors
• LONG AND SHORT MEMORY CONDITIONAL HETEROSKEDASTICITY IN ESTIMATING THE MEMORY PARAMETER OF LEVELS
• The Invariance Principle for Stationary Processes