Robust Inference in Models Identified via Heteroskedasticity
研究异方差识别模型中弱识别问题，提出F检验和稳健推断方法，并应用于高频数据中的货币政策冲击分析，发现日度数据存在弱识别而日内数据识别较强。
Abstract 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 to proportionally, 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 nonconservative 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 provide strong identification.