具有弱代理变量的proxy-SVARs识别与检验策略
An identification and testing strategy for proxy-SVARs with weak proxies
AI总结：
针对弱代理变量下proxy-SVARs的推断问题，提出一种利用非目标冲击的强工具进行识别的策略，并基于自助法重抽样设计预检验，实现标准渐近推断且不受预检验问题影响。
摘要：
当用于识别目标结构性冲击的代理变量（外部工具）较弱时，proxy-SVARs（SVAR-IVs）的推断是非标准的，并且构建感兴趣的脉冲响应的渐近有效置信集需要弱工具稳健方法。在存在多个目标冲击的情况下，检验反转技术除了代理变量所隐含的限制外，还需要对proxy-SVAR参数施加额外限制，这些限制可能难以解释和检验。我们表明，如果proxy-SVAR可以通过对非目标冲击（即分析中非主要关注的冲击）使用“强”工具来识别，则在这些情况下可以通过最小距离估计和标准渐近方法进行频率学派渐近推断。所建议的识别策略依赖于一种新颖的预检验，用于检验工具相关性的原假设，该预检验基于自助法重抽样，不受预检验问题的影响，即检验后渐近推断的有效性不受检验结果的影响。该检验对条件异方差和/或零删失代理变量具有稳健性，计算简单，且无论被工具化的冲击数量多少均适用。一些说明性示例展示了所建议的识别和检验策略的实证实用性。
英文摘要：
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 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, in the sense that the validity of post-test asymptotic inferences is not affected by the outcomes of the test. The test is robust to conditionally heteroskedasticity 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.
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