We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multicollinearity in simultaneous equations system: evaluation of estimation performance of two-parameter estimator.
- Authors
Özbay, Nimet; Toker, Selma
- 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 Özkale and Kaçıranlar (Commun Stat Theory Methods 36(15):2707-2725, <xref>2007</xref>) 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.
- Subjects
SIMULTANEOUS equations; LEAST squares; MULTICOLLINEARITY; RIDGE regression (Statistics); ESTIMATION theory
- Publication
Computational & Applied Mathematics, 2018, Vol 37, Issue 4, p5334
- ISSN
0101-8205
- Publication type
Article
- DOI
10.1007/s40314-018-0628-0