Generalized Least Squares Model Averaging
Generalized Least Squares Model Averaging
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DOI：
10.1080/07474938.2015.1092817
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发表时间：
2016-01-01
影响因子：
1.2
通讯作者：
Yoshimura, Arihiro
中科院分区：
文献类型：
--
作者：
Liu, Qingfeng;Okui, Ryo;Yoshimura, Arihiro
在本文中，我们提出了一种对具有异方差误差的线性回归模型的广义最小二乘估计量进行平均的方法。选择平均权重以最小化 Mallows 的 C-p 类标准。我们证明我们的方法选择的权重向量是最优的。它还表明，即使在估计误差项的方差并对可行的广义最小二乘估计量进行平均时，这种最优性仍然成立。方差可以参数化或非参数化的方式估计。蒙特卡罗模拟结果令人鼓舞。一个实证例子表明，所提出的方法对于预测企业绩效指标非常有用。
In this article, we propose a method of averaging generalized least squares estimators for linear regression models with heteroskedastic errors. The averaging weights are chosen to minimize Mallows' C-p-like criterion. We show that the weight vector selected by our method is optimal. It is also shown that this optimality holds even when the variances of the error terms are estimated and the feasible generalized least squares estimators are averaged. The variances can be estimated parametrically or nonparametrically. Monte Carlo simulation results are encouraging. An empirical example illustrates that the proposed method is useful for predicting a measure of firms' performance.