A Monte Carlo simulation study on partially adaptive estimators of linear regression models

MERT KANTAR Y., USTA İ., ACITAŞ Ş.

JOURNAL OF APPLIED STATISTICS, vol.38, no.8, pp.1681-1699, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 8
  • Publication Date: 2011
  • Doi Number: 10.1080/02664763.2010.516389
  • Journal Name: JOURNAL OF APPLIED STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1681-1699
  • Keywords: linear regression model, non-normal error terms, partially adaptive estimator, sandwich estimator, Monte Carlo simulation, JACKKNIFE, ROBUST
  • Anadolu University Affiliated: Yes

Abstract

This paper presents a comprehensive comparison of well-known partially adaptive estimators (PAEs) in terms of efficiency in estimating regression parameters. The aim is to identify the best estimators of regression parameters when error terms follow from normal, Laplace, Student's t, normal mixture, lognormal and gamma distribution via the Monte Carlo simulation. In the results of the simulation, efficient PAEs are determined in the case of symmetric leptokurtic and skewed leptokurtic regression error data. Additionally, these estimators are also compared in terms of regression applications. Regarding these applications, using certain standard error estimators, it is shown that PAEs can reduce the standard error of the slope parameter estimate relative to ordinary least squares.