Sequential estimation for time series regression models

Takayuki Shiohama, Masanobu Taniguchi

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)295-312
Number of pages18
JournalJournal of Statistical Planning and Inference
Volume123
Issue number2
DOIs
Publication statusPublished - 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

Fingerprint Dive into the research topics of 'Sequential estimation for time series regression models'. Together they form a unique fingerprint.

  • Cite this