Multicollinearity in simultaneous equations system: evaluation of estimation performance of two-parameter estimator

ÖZBAY N., Toker S.

COMPUTATIONAL & APPLIED MATHEMATICS, vol.37, no.4, pp.5334-5357, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 4
  • Publication Date: 2018
  • Doi Number: 10.1007/s40314-018-0628-0
  • Journal Name: COMPUTATIONAL & APPLIED MATHEMATICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.5334-5357
  • Çukurova University Affiliated: Yes

Abstract

In simultaneous equations model, two-stage least squares estimator is easy to apply and commonly preferred. When multicollinearity exists, two-stage least squares estimator has some drawbacks and it is no longer favorable. In this context, biased estimation methods are recommended. Two-parameter estimator of A-zkale and Ka double dagger A +/- ranlar (Commun Stat Theory Methods 36(15):2707-2725, 2007) had been established to be superior to the ordinary least squares estimator under some conditions in linear regression model suffering from multicollinearity. In this paper, the idea of two-parameter estimation in linear regression model is carried out to the simultaneous equations model. For this model, two-stage two-parameter estimator is proposed to remedy the problem of multicollinearity. Estimation performance of this new estimator is evaluated by means of two real-life data analyses. In addition to the numerical example, an extensive Monte Carlo experiment is conducted.