为了实现对自回归模型交叉的统一测试
Jing Zhang1, Yawen Fan2, Yu Wang3
1School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People's Republic of China.
Journal of applied statistics
|December 4, 2024
概括
本研究为自回归 (AR) 模型引入了一种统一的经验概率测试. 新的测试统计统一地汇聚到基平方分布,为静止和非静止过程提供强大的性能.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 对于自回归 (AR) 模型的统一假设测试,特别是关于截取,在统计推理中仍然是一个未解决的挑战.
- 现有的方法往往缺乏通用性,无法适应静止和非静止的AR过程,或具有和没有拦截的模型.
研究的目的:
- 开发一种统一的统计测试,用于自行回归 (AR) 模型中的拦截.
- 解决现有测试的局限性,提供一种适用于静止和非静止AR过程的方法,有或没有拦截.
主要方法:
- 经验概率方法用于构建AR模型截图的新型测试统计数据.
- 测试统计数据的非对称分布在静止和非静止的AR过程中根据零假设得出.
- 在局部替代假设下的非对称分布在特定的温和条件下也在理论上确立了.
主要成果:
- 拟议的经验概率测试统计数据表明在零假设下,分布趋同到标准千平方分布,不论静态性或交叉点的存在.
- 模拟和真实数据示例证实了测试在尺寸准确性和统计能力方面对有限样本的良好表现.
- 衍生出来的非对称性质为测试在各种AR模型规范中的有效性提供了理论基础.
结论:
- 经验概率方法为在广泛的自回归模型中测试拦截提供了一个统一而强大的解决方案.
- 该方法的理论保证和经过实践证明的性能使其成为时间序列分析的宝贵工具.
- 这个统一的测试简化了AR模型的推断,提高了它们在各种领域的适用性,包括计量经济学和信号处理.
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