Estimating functions for nonlinear time series models

S. Ajay Chandra, Masanobu Taniguchi

研究成果: Article査読

26 被引用数 (Scopus)

抄録

This paper discusses the problem of estimation for two classes of nonlinear models, namely random coefficient autoregressive (RCA) and autoregressive conditional heteroskedasticity (ARCH) models. For the RCA model, first assuming that the nuisance parameters are known we construct an estimator for parameters of interest based on Godambe's asymptotically optimal estimating function. Then, using the conditional least squares (CLS) estimator given by Tjøstheim (1986, Stochastic Process. Appl., 21, 251-273) and classical moment estimators for the nuisance parameters, we propose an estimated version of this estimator. These results are extended to the case of vector parameter. Next, we turn to discuss the problem of estimating the ARCH model with unknown parameter vector. We construct an estimator for parameters of interest based on Godambe's optimal estimator allowing that a part of the estimator depends on unknown parameters. Then, substituting the CLS estimators for the unknown parameters, the estimated version is proposed. Comparisons between the CLS and estimated optimal estimator of the RCA model and between the CLS and estimated version of the ARCH model are given via simulation studies.

本文言語English
ページ(範囲)125-141
ページ数17
ジャーナルAnnals of the Institute of Statistical Mathematics
53
1
DOI
出版ステータスPublished - 2001 1月 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率

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