Jackknife model averaging
Jackknife model averaging
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DOI：
10.1016/j.jeconom.2011.06.019
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发表时间：
2012-03-01
影响因子：
6.3
通讯作者：
Racine, Jeffrey S.
中科院分区：
文献类型：
--
作者：
Hansen, Bruce E.;Racine, Jeffrey S.
我们考虑获得适当权重来平均 M 个近似（错误指定）模型的问题，以改进在异方差设置中面对非嵌套模型不确定性时未知条件均值的估计。我们提出了一种“折刀模型平均”（JMA）估计器，它通过最小化交叉验证标准来选择权重。该标准的权重是二次的，因此计算是二次规划的简单应用。我们证明，我们的估计量在实现尽可能低的预期平方误差的意义上是渐近最优的。蒙特卡洛模拟和说明性应用表明，在存在异方差的情况下，JMA 可以比现有模型选择和平均方法显着提高效率。 (C) 2011 Elsevier B.V. 保留所有权利。
We consider the problem of obtaining appropriate weights for averaging M approximate (misspecified) models for improved estimation of an unknown conditional mean in the face of non-nested model uncertainty in heteroskedastic error settings. We propose a "jackknife model averaging" (JMA) estimator which selects the weights by minimizing a cross-validation criterion. This criterion is quadratic in the weights, so computation is a simple application of quadratic programming. We show that our estimator is asymptotically optimal in the sense of achieving the lowest possible expected squared error. Monte Carlo simulations and an illustrative application show that JMA can achieve significant efficiency gains over existing model selection and averaging methods in the presence of heteroskedasticity. (C) 2011 Elsevier B.V. All rights reserved.