具有异方差误差项的线性回归模型的系数估计量的性质

Lietuvos Matematikos Rinkinys Pub Date : 2023-09-21 DOI:10.15388/lmr.2006.30725

Alfredas Račkauskas, Danas Zuokas

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引用次数: 0

摘要

本文给出了y = β x + ε线性回归模型中干扰项可以是异方差的系数向量β的估计广义最小二乘估计。对于变化段类型的异方差，我们使用蒙特卡罗方法研究了所提出的最小二乘(OLS)估计量和普通最小二乘估计量的经验性质。结果表明，EGLS估计量的经验协方差小于OLS估计量。

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Properties of the coefficient estimators for the linear regression model with heteroskedastic error term

In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic. For the heteroskedasticity of the changed segment type, using Monte-Carlo method, we investigate empirical properties of the proposed and ordinary least squares (OLS) estimators. The results show that the empirical covariance of the EGLS estimators is smaller than that of OLS estimators.

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