弱点识别 强大的测试用于使用暗示概率的子向量
Marine Carrasco1, Saraswata Chaudhuri2
1Department of Economics, University of Montreal, Montreal, QC H3T 1J4, Canada.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一项新的统计测试,以解决在识别薄弱的模型中参数估计的问题. 这种新的方法提高了准确性,并揭示了退伍军人身份对收入的负面影响.
科学领域:
- 计量经济学 计量经济学
- 统计推理 统计推理
- 假设测试 假设测试
背景情况:
- 传统的统计测试 (瓦尔德,概率比,得分) 在识别较弱的模型中显示过度拒绝.
- 软弱的识别对计量经济学中可靠的假设测试提出了挑战.
- 现有的方法在弱识别场景下缺乏精细的有限样本性能.
研究的目的:
- 开发一个可靠的统计测试假设关于参数子向量在时刻条件模型.
- 克服尺寸扭曲并改善在弱识别条件下常规测试的有限样本性能.
- 引入一种新的基于预测的两步修改得分测试,使用信息理论标准.
主要方法:
- 将基于投影的测试扩展到一个修改的得分测试,其中包含信息理论标准的暗示概率.
- 一个两步程序:参数空间缩小,然后进行修改的得分测试.
- 对概括经验概率暗示概率类的非对称性质的推导.
主要成果:
- 拟议的测试证明了模拟研究中非常好的有限样本大小和功率.
- 该方法有效地解决了与虚弱识别相关的过度拒绝问题.
- 对退伍军人收入数据的应用表明退伍军人身份的负面影响.
结论:
- 开发的修改得分测试提供了一个可靠的解决方案,用于在模型中测试假设的弱识别.
- 这种两步方法提高了统计准确性和有限样本特性.
- 经验发现表明,退伍军人身份对收入有不利影响,需要进一步调查.
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