ASYMPTOTIC THEORY FOR MAXIMUM LIKELIHOOD ESTIMATION OF THE MEMORY PARAMETER IN STATIONARY GAUSSIAN PROCESSES

DOI10.1017/S0266466611000399zbMath1298.62044OpenAlexW2084879996MaRDI QIDQ5389961

Offer Lieberman, Roy Rosemarin, Judith Rousseau

Publication date: 24 April 2012

Published in: Econometric Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0266466611000399

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Gaussian processes (60G15) Stationary stochastic processes (60G10)

Related Items (10)

Optimal bias correction of the log-periodogram estimator of the fractional parameter: a jackknife approach ⋮ BIAS CORRECTION OF SEMIPARAMETRIC LONG MEMORY PARAMETER ESTIMATORS VIA THE PREFILTERED SIEVE BOOTSTRAP ⋮ Asymptotically efficient estimators for self-similar stationary Gaussian noises under high frequency observations ⋮ Optimal estimation of the rough Hurst parameter in additive noise ⋮ Corrigendum: Error bounds and asymptotic expansions for Toeplitz product functionals of unbounded spectra ⋮ Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process ⋮ Asymptotics for the Conditional‐Sum‐of‐Squares Estimator in Multivariate Fractional Time‐Series Models ⋮ Local asymptotic normality property for fractional Gaussian noise under high-frequency observations ⋮ Pseudo-maximum likelihood estimators in linear regression models with fractional time series ⋮ Shrinkage Estimation of the Memory Parameter in Stationary Gaussian Processes

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