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分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接讨论代理 SVAR、外部工具变量识别结构冲击及脉冲响应推断。

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关注这篇论文的作者
Giovanni Angelini
Giuseppe Cavaliere
Luca Fanelli

弱代理变量下代理 SVAR 的识别与检验策略

研究问题
目标结构冲击的外部工具变量较弱时,能否借助非目标冲击的强工具变量识别代理 SVAR,并恢复常规渐近推断?
方法与思路
作者以非目标冲击的强工具变量进行识别和最小距离估计,提出工具相关性的 bootstrap 预检验,并利用统计量渐近独立性。
材料陈述的贡献
摘要称,满足该识别策略时可采用标准渐近方法,预检验结果不影响后续推断有效性,且检验允许条件异方差或零截尾代理。
阅读关注点
核对非目标强工具变量如何识别目标冲击、预检验原假设与强度条件,以及渐近独立性和多冲击情况下的限制。
阅读边界
仅据所给 OpenAlex 摘要,不能断言所有弱代理问题都被解决;结论依赖非目标强工具变量可用及未列明的完整识别条件。

原文摘要

When proxies (external instruments) used to identify target structural shocks are weak, inference in proxy-SVARs (SVAR-IVs) is nonstandard and the construction of asymptotically valid confidence sets for the impulse responses of interest requires weak-instrument robust methods. In the presence of multiple target shocks, test inversion techniques require extra restrictions on the proxy-SVAR parameters other than those implied by the proxies that may be difficult to interpret and test. We show that frequentist asymptotic inference in these situations can be conducted through Minimum Distance estimation and standard asymptotic methods if the proxy-SVAR can be identified by using ‘strong’ instruments for the non-target shocks; i.e., the shocks which are not of primary interest in the analysis. The suggested identification strategy hinges on a novel pre-test for the null of instrument relevance, based on bootstrap resampling, which is not subject to pre-testing issues. Specifically, the validity of post-test asymptotic inferences remains unaffected by the test outcomes due to an asymptotic independence result between the bootstrap and non-bootstrap statistics. The test is robust to conditionally heteroskedastic and/or zero-censored proxies, is computationally straightforward and applicable regardless of the number of shocks being instrumented. Some illustrative examples show the empirical usefulness of the suggested identification and testing strategy.

笔记与分组
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