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313819

Interval Estimation for Amplitude-Dependent Exponential Autoregressive (EXTRA) Models

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Last updated: 05 Jan 2025

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Abstract

Jones (1976) and Ozaki and Oda (1978) independently introduced a class of nonlinear models known as amplitude-dependent exponential autoregressive (EXPAR) models. Many authors have discussed the usefulness of these models (e.g. Qzaki, 1993). The conditional least squares method has been used frequently to get point estimates for the unknown parameters of these models. However, problems have been raised because of the exponential regression type of the EXPAR models. In this paper, the bootstrap algorithm (Efron, 1979) is employed to estimate the standard errors of the conditional least squares estimates and construct 100 (1-a) % confidence intervals for the unknown parameters of the exponential autoregressive (EXPAR) models. Simulation results are presented to motivate using the bootstrap procedure. A real example, using the famous Canadian lynx data, is given.  

DOI

10.21608/esju.2000.313819

Keywords

Non-Linear Models - Amplitude-Dependent Exponential Autoregressive (EXPAR) Models, Bootstrap - Conditional Least Squares - Confidence Intervals - Lynx Data

Volume

44

Article Issue

1

Related Issue

43031

Issue Date

2000-06-01

Publish Date

2000-06-01

Page Start

1

Page End

10

Print ISSN

0542-1748

Online ISSN

2786-0086

Link

https://esju.journals.ekb.eg/article_313819.html

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https://esju.journals.ekb.eg/service?article_code=313819

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Original Article

Type Code

1,914

Publication Type

Journal

Publication Title

The Egyptian Statistical Journal

Publication Link

https://esju.journals.ekb.eg/

MainTitle

Interval Estimation for Amplitude-Dependent Exponential Autoregressive (EXTRA) Models

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Article

Created At

28 Dec 2024