Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging
Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging
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DOI：
10.3390/econometrics1020141
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发表时间：
2013-07
期刊：
影响因子：
1.5
通讯作者：
N. Sueishi
中科院分区：
文献类型：
--
作者：
N. Sueishi
本文发展了力矩约束模型的模型选择和平均方法。我们首先提出了一个基于广义经验似然估计的聚焦信息准则。我们解决的问题，选择一个最佳的模型，而不是一个正确的模型，估计一个特定的参数的兴趣。然后，本研究探讨一个广义经验似然模型平均估计，最大限度地减少渐近均方误差。模拟研究表明，我们的平均估计可以是一个有用的替代现有的后选择估计。
This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a specific parameter of interest. Then, this study investigates a generalized empirical likelihood-based model averaging estimator that minimizes the asymptotic mean squared error. A simulation study suggests that our averaging estimator can be a useful alternative to existing post-selection estimators.