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ESTIMATION IN LONG‐MEMORY TIME SERIES MODEL
Author(s) -
Kashyap R. L.,
Eom KieBum
Publication year - 1988
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1988.tb00451.x
Subject(s) - mathematics , estimator , series (stratigraphy) , mean squared error , statistics , bias of an estimator , least squares function approximation , long memory , estimation theory , algorithm , minimum variance unbiased estimator , econometrics , volatility (finance) , paleontology , biology
This study deals with the parameter estimation in long‐memory time series models. An unbiased and consistent estimator is proposed. The proposed estimator is based on a least‐squares method in the frequency domain, and it is computationally simple. Also, the Cramer–Rao lower bound is derived. The mean‐square error of the proposed estimator is order of O(1/ N ), where N is the number of samples. The accuracy of the estimates is verified using synthetic long‐memory time series data.