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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究非负时间序列,并明确聚焦交易次数及金融事件间隔等事件级交易数据,支持时间序列与高频计量分类。

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代码、数据与补充材料

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关注这篇论文的作者
Jiti Gao
Nam Hyun Kim
Patrick W. Saart

非负时间序列乘法误差模型的设定错误检验

研究问题
针对成交量、交易次数及金融事件等待时间等非负序列,如何检验乘法误差模型的设定,并理解其点过程含义?
方法与思路
作者提出替代性的乘法误差模型设定检验,重点讨论金融事件等待时间的情形,并涉及不可观测变量的非参数检验。
材料陈述的贡献
摘要称新检验对金融事件等待时间模型尤其有用,其结果关联点过程的基本概念,并推进不可观测变量的非参数检验。
阅读关注点
应核对原假设、统计量及潜在误差的估计方式,确认等待时间模型与点过程之间的对应,以及临界值和检验功效。
阅读边界
仅据所给出版社摘要,无法确定具体统计量、有效性条件或相较已有检验的数值表现;点过程含义也需全文核实。

原文摘要

In recent years, analysis of financial time series focuses largely on data related to market trading activity. Apart from modeling of the conditional variance of returns within the generalized autoregressive conditional heteroskedasticity (GARCH) family of models, presently attention is also devoted to that of other market variables, for instance volumes, number of trades or financial durations. To this end, a large group of researchers focus their studies on a class of model that is referred to in the literature as the multiplicative error model (MEM), which is considered particularly for modeling non-negative time series processes. The goal of the current paper is to establish an alternative misspecification test for the MEM of non-negative time series processes. In the literature, although several procedures are available to perform hypothesis testing for the MEM, the newly proposed testing procedure is particularly useful in the context of the MEM of waiting times between financial events since its outcomes have a number of important implications on the fundamental concept of point processes. Finally, the current paper makes a number of statistical contributions, especially in making a head way into nonparametric hypothesis testing of unobservable variables.

笔记与分组
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