How to construct monthly VAR proxies based on daily surprises in futures markets

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2024-09-03 DOI:10.1016/j.jedc.2024.104966
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Abstract

It is common in applied work to estimate responses of macroeconomic aggregates to news shocks derived from surprise changes in daily futures prices around the date of policy announcements. This requires mapping the daily surprises into a monthly shock that may be used as an external instrument in a monthly VAR model or local projection. The standard approach has been to sum these daily surprises over the course of a given month when constructing the monthly proxy variable, ignoring the accounting relationship between daily and average monthly price data. In this paper, I discuss an alternative approach to constructing monthly proxies from daily surprises that takes account of this link and revisit the question of how to use OPEC announcements to identify news shocks in VAR models of the global oil market. The proposed approach calls into question the interpretation of the identified shock as oil supply news and implies quantitatively and qualitatively different estimates of the macroeconomic impact of OPEC announcements.

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如何根据期货市场的每日意外事件构建月度 VAR 代用指标
在应用工作中,通常会估算宏观经济总量对政策公布日前后每日期货价格意外变化所产生的新闻冲击的反应。这就需要将每日意外事件映射为月度冲击,在月度 VAR 模型或本地预测中作为外部工具使用。标准的方法是,在构建月度替代变量时,将给定月份内的每日意外加总,忽略每日价格数据与月度平均价格数据之间的核算关系。在本文中,我讨论了从每日意外事件中构建月度替代变量的另一种方法,这种方法考虑到了这种联系,并重新探讨了如何在全球石油市场的 VAR 模型中使用欧佩克公告来识别新闻冲击的问题。所提出的方法对将所识别的冲击解释为石油供应新闻提出了质疑,并意味着对欧佩克公告的宏观经济影响的估计在数量和质量上有所不同。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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