Maximum likelihood estimators of a long-memory process from discrete observations
نویسندگان
چکیده
منابع مشابه
Maximum likelihood estimators and random walks in long memory models
We consider statistical models driven by Gaussian and non-Gaussian self-similar processes with long memory and we construct maximum likelihood estimators (MLE) for the drift parameter. Our approach is based in the non-Gaussian case on the approximation by random walks of the driving noise. We study the asymptotic behavior of the estimators and we give some numerical simulations to illustrate ou...
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Asymptotic properties of MLEs and QMLEs of mixed regressive, spatial autoregressive models are investigated. The stochastic rates of convergence of the MLE and QMLE for such models may be less than the √ n-rate under some circumstances even though its limiting distribution is asymptotically normal. When spatially varying regressors are relevant, the MLE and QMLE of the mixed regressive, autoreg...
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We develop a new class of Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models which are useful for modelling regularly-spaced and irregularly-spaced discrete-time long-memory data. We derive the autocovariance function of a stationary CARFIMA model, and study maximum likelihood estimation of a regression model with CARFIMA errors, based on discrete-time data ...
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ژورنال
عنوان ژورنال: Advances in Difference Equations
سال: 2018
ISSN: 1687-1847
DOI: 10.1186/s13662-018-1611-1