Extrapolations in non-linear autoregressive processes

Anděl, Jiří, and Dupač, Václav. "Extrapolations in non-linear autoregressive processes." Kybernetika 35.3 (1999): [383]-389. <http://eudml.org/doc/33434>.

@article{Anděl1999,
abstract = {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.},
author = {Anděl, Jiří, Dupač, Václav},
journal = {Kybernetika},
keywords = {least-squares extrapolation},
language = {eng},
number = {3},
pages = {[383]-389},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Extrapolations in non-linear autoregressive processes},
url = {http://eudml.org/doc/33434},
volume = {35},
year = {1999},
}

TY - JOUR
AU - Anděl, Jiří
AU - Dupač, Václav
TI - Extrapolations in non-linear autoregressive processes
JO - Kybernetika
PY - 1999
PB - Institute of Information Theory and Automation AS CR
VL - 35
IS - 3
SP - [383]
EP - 389
AB - 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.
LA - eng
KW - least-squares extrapolation
UR - http://eudml.org/doc/33434
ER -