Identification via heteroskedasticity exploits variance changes between regimes to identify parameters in simultaneous equations. Weak identification occurs when shock variances change very little or multiple variances change close-toproportionally, making standard inference unreliable. I propose an F-test for weak identification in a common simple version of the model. More generally, I establish conditions for validity of non-conservative robust inference on subsets of the parameters, which can be used to test for weak identification. I study monetary policy shocks identified using heteroskedasticity in high frequency data. I detect weak identification, invalidating standard inference, in daily data, while intraday data provides strong identification.

中文翻译：

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通过异方差识别的模型中的稳健推理

通过异方差的识别利用制度之间的方差变化来识别联立方程中的参数。当冲击方差变化很小或多个方差几乎成比例地变化时，就会出现弱识别，从而使标准推断不可靠。我建议在模型的常见简单版本中对弱识别进行 F 检验。更一般地说，我为参数子集的非保守鲁棒推理的有效性建立了条件，可用于测试弱识别。我研究了使用高频数据中的异方差确定的货币政策冲击。我在日常数据中检测到弱识别，使标准推理无效，而日内数据提供了强识别。