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

ÖZBAY N. , Toker S.

COMPUTATIONAL & APPLIED MATHEMATICS, cilt.37, ss.5334-5357, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 37 Konu: 4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s40314-018-0628-0
  • Sayfa Sayıları: ss.5334-5357


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.