Model averaging by jackknife criterion in models with dependent data
Model averaging by jackknife criterion in models with dependent data
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DOI：
10.1016/j.jeconom.2013.01.004
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发表时间：
2013-06
影响因子：
6.3
通讯作者：
Xinyu Zhang;Alan T. K. Wan;Guohua Zou
中科院分区：
文献类型：
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作者：
Xinyu Zhang;Alan T. K. Wan;Guohua Zou
在过去的十年中，出现了用频率论方法进行模型平均的文献。在大多数情况下，已有的各种频率模型平均估计器在i.i.d误差下都具有渐近最优性。最近，Hansen和Racine [Hansen， b.e., Racine， J., 2012]。折刀模型平均。[j] [Journal of Econometrics][167, 38-46]开发了一种叠刀模型平均（JMA）估计器，它比其竞争对手具有重要的优势，因为它在异方差误差下实现了尽可能低的渐近平方误差。在本文中，我们扩大了Hansen和Racine的分析范围，以涵盖具有(i)非对角误差协方差结构和（ii）滞后因变量的模型，从而允许依赖数据。我们表明，在这些设置下，JMA估计量是渐近最优的，与Hansen和Racine使用的标准等效。蒙特卡罗研究证明了JMA估计器在各种模型设置下的有限样本性能。
The past decade witnessed a literature on model averaging by frequentist methods. For the most part, the asymptotic optimality of various existing frequentist model averaging estimators has been established under i.i.d. errors. Recently, Hansen and Racine [Hansen, B.E., Racine, J., 2012. Jackknife model averaging. Journal of Econometrics 167, 38–46] developed a jackknife model averaging (JMA) estimator, which has an important advantage over its competitors in that it achieves the lowest possible asymptotic squared error under heteroscedastic errors. In this paper, we broaden Hansen and Racine’s scope of analysis to encompass models with (i) a non-diagonal error covariance structure, and (ii) lagged dependent variables, thus allowing for dependent data. We show that under these set-ups, the JMA estimator is asymptotically optimal by a criterion equivalent to that used by Hansen and Racine. A Monte Carlo study demonstrates the finite sample performance of the JMA estimator in a variety of model settings.