Stochastic regression model with dependent disturbances

Kokyo Choy, Masanobu Taniguchi

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In this paper, we consider the estimation of the coefficient of a stochastic regression model whose explanatory variables and disturbances are permitted to exhibit short-memory or long-memory dependence. Three estimators of the coefficient are proposed. A variety of their asymptotics are illuminated under various assumptions on the explanatory variables and the disturbances. Numerical studies of the theoretical results are given. They show some unexpected aspects of the asymptotics of the three estimators.

Original languageEnglish
Pages (from-to)175-196
Number of pages22
JournalJournal of Time Series Analysis
Volume22
Issue number2
Publication statusPublished - 2001 Mar
Externally publishedYes

Fingerprint

Stochastic Model
Regression Model
Disturbance
Estimator
Data storage equipment
Dependent
Long Memory
Coefficient
Numerical Study
Regression model
Coefficients
Long memory

Keywords

  • Best linear unbiased estimator (BLUE)
  • Least squares estimator (LSE)
  • Long-memory process
  • Ratio estimator (RE)
  • Short-memory process
  • Spectral density
  • Stationary linear process
  • Stochastic regression model

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability

Cite this

Stochastic regression model with dependent disturbances. / Choy, Kokyo; Taniguchi, Masanobu.

In: Journal of Time Series Analysis, Vol. 22, No. 2, 03.2001, p. 175-196.

Research output: Contribution to journalArticle

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