Optimal estimator under risk matrix in a seemingly unrelated regression model and its generalized least squares expression

Shun Matsuura, Hiroshi Kurata

Research output: Contribution to journalArticlepeer-review

Abstract

A set of multiple regression models whose error terms have possibly contemporaneous correlations is called a seemingly unrelated regression model. In this paper, a best equivariant estimator of the regression vector under risk matrix is established in a seemingly unrelated regression model. It should be noted that an estimator optimal with respect to risk matrix remains optimal under a broad range of quadratic loss functions. A generalized least squares expression of our estimator is also presented.

Original languageEnglish
JournalStatistical Papers
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Elliptically symmetric distribution
  • Equivariant estimator
  • Generalized least squares
  • Risk matrix
  • Seemingly unrelated regression model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Optimal estimator under risk matrix in a seemingly unrelated regression model and its generalized least squares expression'. Together they form a unique fingerprint.

Cite this