Generalized Minimum Distance Estimators in Linear Regression with Dependent Errors

TL;DR

This paper introduces a minimum distance estimation method for linear regression models with dependent, strongly mixing errors, demonstrating its superior performance through simulation compared to other estimators.

Contribution

It extends minimum distance estimation to linear regression with dependent errors and provides asymptotic properties and empirical comparisons.

Findings

Minimum distance estimator outperforms other estimators in simulations.

Asymptotic distributional properties are established for the estimators.

The KoulMde R package facilitates implementation and comparison.

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.