Sequential estimation for time series regression models

Takayuki Shiohama; Masanobu Taniguchi

doi:10.1016/S0378-3758(03)00153-8

Sequential estimation for time series regression models

Takayuki Shiohama^*, Masanobu Taniguchi

^*Corresponding author for this work

School of Fundamental Science and Engineering

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Sequential procedures are proposed to estimate the regression parameters in a linear regression model with dependent residuals. The error process considered here is a linear process with unknown spectral density. The sequential point estimator for the regression parameters is based on the least-squares estimator and is shown to be asymptotically risk efficient under some natural conditions on the design sequence. Simulation studies are given to evaluate the asymptotic performances of the sequential procedures of the sequential estimator.

Original language	English
Pages (from-to)	295-312
Number of pages	18
Journal	Journal of Statistical Planning and Inference
Volume	123
Issue number	2
DOIs	https://doi.org/10.1016/S0378-3758(03)00153-8
Publication status	Published - 2004 Jul 1

Keywords

Least-squares estimator
Linear process
Sequential procedure
Stopping rule
Time series regression model

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/S0378-3758(03)00153-8

Cite this

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AU - Taniguchi, Masanobu

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AB - Sequential procedures are proposed to estimate the regression parameters in a linear regression model with dependent residuals. The error process considered here is a linear process with unknown spectral density. The sequential point estimator for the regression parameters is based on the least-squares estimator and is shown to be asymptotically risk efficient under some natural conditions on the design sequence. Simulation studies are given to evaluate the asymptotic performances of the sequential procedures of the sequential estimator.

KW - Least-squares estimator

KW - Linear process

KW - Sequential procedure

KW - Stopping rule

KW - Time series regression model

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EP - 312

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 2

ER -

Sequential estimation for time series regression models

Abstract

Keywords

ASJC Scopus subject areas

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