用可扩展马尔科夫链蒙特卡洛方法进行大时间变化的参数回归的动态缩小前值.
Niko Hauzenberger1,2, Florian Huber2, Gary Koop1
1Department of Economics, University of Strathclyde, Glasgow, UK.
概括
本研究引入了变时参数 (TVP) 模型的动态收缩前置,使高维数据的有效分析成为可能. 这种新的方法准确地识别了稀疏的参数变化,并改善了预测性能.
科学领域:
- 计量经济学 计量经济学
- 统计建模 统计建模
背景情况:
- 时间变量参数 (TVP) 回归模型通常涉及多个系数,要求对可靠推断进行仔细的预先规范.
- 马尔科夫链蒙特卡洛 (MCMC) 方法的计算局限性限制了它们的应用到具有有限数量的预测器的模型.
研究的目的:
- 为TVP模型提出一种新的动态收缩先验,以解释时间变化系数的稀疏性.
- 开发一个可扩展的MCMC算法,有效地处理高维TVP回归和TVP向量自回归.
主要方法:
- 动态收缩前景的发展反映了系数时间变化的插曲性.
- 实现了一个可扩展的马尔科夫链蒙特卡洛 (MCMC) 算法,用于高维应用.
主要成果:
- 使用人工数据证明了拟议方法的准确性和计算效率.
- 在实际应用中对欧元区利率期限结构的稀疏参数变化进行有效识别.
结论:
- 动态收缩前线有效地捕捉了高维模型中的稀疏时间变化.
- 开发的可扩展的MCMC算法为复杂的TVP模型提供了计算优势,从而提高了预测准确度.
关键词:
贝叶斯的变量选择选择是贝叶斯的.在C11上,你会看到C11.在C3030中,它是C30.在C5050中,你会发现C50是什么?E3 E3 的意思是什么?E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43 E43在动态收缩之前的动态收缩.全球-本地收缩之前之前的收缩.可扩展的马尔科夫链蒙特卡洛时间变化的参数回归.更多相关视频
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