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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

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AI 根据所提供的论文材料归类:摘要的明确方法对象是具有未观测交互效应的面板结构突变检验与估计。

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关注这篇论文的作者
Yiannis Karavias
Joakim Westerlund

交互效应面板中的结构突变与股市对 COVID-19 的反应

研究问题
在受到重大共同事件影响的交互效应面板中,如何检验结构突变、估计日期并构造区间,以研究股市与疫情关系的变化?
方法与思路
作者提出包含结构变化检验、突变日期估计及其置信区间的工具组,并应用于二〇二〇年六十一个国家的股市面板。
材料陈述的贡献
摘要称,工具适用于不同规模面板并应对一般未观测异质性;应用检测到四月第一周的突变,疫情影响此前为负、此后为零。
阅读关注点
应核对交互效应与误差条件、突变检验和日期区间的构造,并检查疫情变量、同期政策及其他共同冲击如何进入应用模型。
阅读边界
仅据所给 OpenAlex 版本摘要;稳健条件和效应数值未说明,量化宽松只是作者提出的可能解释,不能据此确认为因果机制。

原文摘要

Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March. Supplementary materials for this article are available online.

笔记与分组
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