A central limit theorem for estimation in Gaussian stationary time series observed at unequally spaced times

DOI10.1016/0304-4149(83)90005-4zbMath0502.62073OpenAlexW2024075606MaRDI QIDQ1172908

William T. M. Dunsmuir

Publication date: 1983

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(83)90005-4

zbMATH Keywords

asymptotic normalitymartingale differencesmaximum likelihood estimatormissing dataGaussian stationary time seriesunequally spaced times

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Non-Markovian processes: estimation (62M09)

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Correlation testing in time series, spatial and cross-sectional data ⋮ Matrix representations of spectral coefficients of randomly sampled ARMA models ⋮ Gaussian estimation of first order time series models with Bernoulli observations ⋮ ON DISCRETE SAMPLING OF TIME-VARYING CONTINUOUS-TIME SYSTEMS ⋮ Likelihood ratio type unit root tests for ar(1)models with nonconsecutive observations ⋮ TESTING FOR A UNIT ROOT IN AN AR(1) TIME SERIES USING IRREGULARLY OBSERVED DATA ⋮ Least squares estimation of ARCH models with missing observations ⋮ Non-nested testing of spatial correlation ⋮ ON THE ASYMPTOTIC EFFICIENCY OF ESTIMATORS OF THE PARAMETERS OF AN ARMA PROCESS ⋮ Unnamed Item

Cites Work

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• Estimation of a time series model from unequally spaced data
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• Vector linear time series models
• Martingale Central Limit Theorems

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