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Title: Extrapolations in non-linear autoregressive processes (English)
Author: Anděl, Jiří
Author: Dupač, Václav
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 35
Issue: 3
Year: 1999
Pages: [383]-389
Summary lang: English
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Category: math
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Summary: We derive a formula for $m$-step least-squares extrapolation in non-linear AR$(p)$ processes and compare it with the naïve extrapolation. The least- squares extrapolation depends on the distribution of white noise. Some bounds for it are derived that depend only on the expectation of white noise. An example shows that in general case the difference between both types of extrapolation can be very large. Further, a formula for least-squares extrapolation in multidimensional non-linear AR($p$) process is derived. (English)
Keyword: least-squares extrapolation
MSC: 62M10
MSC: 62M20
idZBL: Zbl 1274.62570
idMR: MR1704673
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Date available: 2009-09-24T19:26:31Z
Last updated: 2015-03-27
Stable URL: http://hdl.handle.net/10338.dmlcz/135294
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Reference: [1] Anděl J.: A remark on least–squares and naïve extrapolations in non–linear AR(1) processes.J. Forecasting 15 (1996), 549–552 10.1002/(SICI)1099-131X(199612)15:7<549::AID-FOR630>3.0.CO;2-F
Reference: [2] Kall P., Wallace S. W.: Stochastic Programming.Wiley, Chichester 1994 Zbl 0812.90122, MR 1315300
Reference: [3] Tong H.: Non–linear Time Series.Clarendon Press, Oxford 1990 Zbl 0835.62076
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