非高斯冲击下代理SVAR的识别与推断

中文摘要 AI 辅助

识别结构VAR的两种常用方法是外部工具变量（其具有经济内容但往往较弱）和冲击的非高斯性（其提供统计识别但不具有经济意义）。我们将这两种策略结合在一个统一的广义矩方法框架中，该框架将代理排除限制与结构冲击的高阶矩条件叠加在一起。这种混合方法在点识别目标冲击的同时，也以符号和排序为限识别非目标冲击。在适当的秩条件下，高阶矩在工具强度上一致地锚定识别。因此，在局部趋零代理相关性下，动态因果效应的估计量保持一致，标准渐近推断仍然有效。此外，Anderson-Rubin置信集比仅使用工具的对应置信集大幅变窄。混合估计量也比单独使用任一识别来源更有效：在任意固定的代理相关性下，即使是弱工具也通过其与非高斯矩块的协方差提高估计量的效率。在代理外生性的局部偏离下，我们提供了渐近偏差界，并表明更强的冲击非高斯性压缩了偏差。最后，过度识别结构产生两个相互正交的规格检验，分别针对代理外生性和高阶矩条件的有效性。我们推导了它们的极限分布，并提供了有限样本临界值的自助法程序。蒙特卡洛模拟和两个应用（识别石油新闻冲击和欧元区货币政策冲击）证明了我们框架的潜力。

英文摘要

Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework that stacks proxy exclusion restrictions with higher-order moment conditions of the structural shocks. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank conditions, the higher-order moments anchor the identification uniformly over the instrument strength. Consequently, under local-to-zero proxy relevance, estimators of the dynamic causal effects remain consistent, and standard asymptotic inference remains valid. Moreover, the Anderson-Rubin confidence sets are substantially narrower than their instrument-only counterparts. The hybrid estimator is also more efficient than either source of identification used in isolation: at any fixed proxy relevance, even a weak instrument increases efficiency of the estimator through its covariance with the non-Gaussian moment block. Under local deviations from proxy exogeneity, we provide asymptotic bias bounds and show that stronger non-Gaussianity of the shocks compresses the bias. Finally, the over-identified structure yields two mutually orthogonal specification tests, for proxy exogeneity and validity of higher-order moment conditions. We derive their limiting distributions and provide a bootstrap procedure for finite-sample critical values. Monte Carlo simulations and two applications with identification of oil news-shock and a Euro-area MP shock demonstrate the potential of our framework.

发表机构

• University of Bologna（博洛尼亚大学）

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