Nonparametric Identification of Nonlinear Time Series: Projections
作者:
Dag Tjøstheim,
BjørnH. Auestad,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 428
页码: 1398-1409
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476879
出版商: Taylor & Francis Group
关键词: Additive;Asymptotic bias;Conditional mean;Conditional variance;Kernel estimation
数据来源: Taylor
摘要:
We study the possibility of identifying general linear and nonlinear time series models using nonparametric methods. The kernel estimators of the conditional mean and variance are used as a basis, and the properties of these quantities as model indicators are briefly discussed. Some drawbacks are pointed out, and motivated by these we introduce projections as tools of identification. The projections are especially useful for additive modeling. Expressions for the asymptotic bias and variance are obtained. The projection of the conditional variance is suggested as a tool for identifying heteroscedastic time series. The results are illustrated by simulations for both the estimators of the projections and the estimators of the conditional mean and variance.
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