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Estimation and forecasting of long‐memory processes with missing values
Author(s) -
Palma Wilfredo,
Chan Ngai Hang
Publication year - 1997
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199711)16:6<395::aid-for660>3.0.co;2-p
Subject(s) - autoregressive fractionally integrated moving average , missing data , kalman filter , state space representation , representation (politics) , computer science , series (stratigraphy) , estimation , econometrics , set (abstract data type) , data mining , artificial intelligence , algorithm , mathematics , long memory , machine learning , economics , volatility (finance) , paleontology , management , politics , political science , law , biology , programming language
This paper addresses the issues of maximum likelihood estimation and forecasting of a long‐memory time series with missing values. A state‐space representation of the underlying long‐memory process is proposed. By incorporating this representation with the Kalman filter, the proposed method allows not only for an efficient estimation of an ARFIMA model but also for the estimation of future values under the presence of missing data. This procedure is illustrated through an analysis of a foreign exchange data set. An investment scheme is developed which demonstrates the usefulness of the proposed approach. © 1997 John Wiley & Sons, Ltd.