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Finite sample prediction and interpolation for ARIMA models with missing data
Author(s) -
Penzer Jeremy,
Shea Brian
Publication year - 1999
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(199911)18:6<411::aid-for737>3.0.co;2-d
Subject(s) - missing data , cholesky decomposition , autoregressive integrated moving average , series (stratigraphy) , likelihood function , mathematics , interpolation (computer graphics) , transformation (genetics) , sample (material) , statistics , econometrics , computer science , time series , maximum likelihood , artificial intelligence , paleontology , motion (physics) , biochemistry , eigenvalues and eigenvectors , physics , chemistry , chromatography , quantum mechanics , gene , biology
A transformation which allows Cholesky decomposition to be used to evaluate the exact likelihood function of an ARIMA model with missing data has recently been suggested. This method is extended to allow calculation of finite sample predictions of future observations. The output from the exact likelihood evaluation may also be used to estimate missing series values. Copyright © 1999 John Wiley & Sons, Ltd.