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Finite sample prediction and interpolation for ARIMA models with missing data

✍ Scribed by Jeremy Penzer; Brian Shea

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 133 KB
Volume: 18
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199911)18:6<411::aid-for737>3.0.co;2-d

No coin nor oath required. For personal study only.

✦ Synopsis

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 ®nite sample predictions of future observations. The output from the exact likelihood evaluation may also be used to estimate missing series values.

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