A Gini estimator for regression with autocorrelated errors

IF 0.7 4区经济学 Q3 ECONOMICS

Studies in Nonlinear Dynamics and Econometrics Pub Date : 2022-03-24 DOI:10.1515/snde-2020-0134

Ndéné Ka, Stéphane Mussard

引用次数: 0

Abstract

Abstract The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.

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自相关误差回归的Gini估计

摘要广泛应用于序列相关线性回归模型参数估计的Prais-Winsten技术对异常值非常敏感。本文提出了一种基于基尼均值差(GMD)的替代方法。蒙特卡罗模拟表明，当数据被异常值污染时，基尼估计器比一般的最小二乘估计器更稳健。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Studies in Nonlinear Dynamics and Econometrics Multiple-

CiteScore

1.40

自引率

12.50%

发文量

期刊介绍： Studies in Nonlinear Dynamics & Econometrics (SNDE) recognizes that advances in statistics and dynamical systems theory may increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.