Generalized Minimum Distance Estimators in Linear Regression with Dependent Errors
Statistics Theory
2017-01-06 v1 Statistics Theory
Abstract
This paper discusses minimum distance estimation method in the linear regression model with dependent errors which are strongly mixing. The regression parameters are estimated through the minimum distance estimation method, and asymptotic distributional properties of the estimators are discussed. A simulation study compares the performance of the minimum distance estimator with other well celebrated estimator. This simulation study shows the superiority of the minimum distance estimator over another estimator. KoulMde (R package) which was used for the simulation study is available online. See section 4 for the detail.
Cite
@article{arxiv.1701.01199,
title = {Generalized Minimum Distance Estimators in Linear Regression with Dependent Errors},
author = {Jiwoong Kim},
journal= {arXiv preprint arXiv:1701.01199},
year = {2017}
}