Prediction with incomplete past and interpolation of missing values

A generalized innovation algorithm is used to solve the problems of prediction of future values based on incomplete past and interpolation of missing values of a stationary time series. The emphasis is on the computational aspects and the proposed method is particularly useful when there are several missing values with arbitrary patterns.

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Year of publication:	1997
Authors:	Cheng, R. ; Pourahmadi, M.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 33.1997, 4, p. 341-346
Publisher:	Elsevier
Keywords:	Stationary time series Innovation algorithm Autoregressive parameters Variance of errors

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005254666