Identification of Structural Vector Autoregressions by Stochastic Volatility

We propose to exploit stochastic volatility for statistical identification of structural vector autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for maximum likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroscedastic SVARs. We apply the model to study the importance of oil supply shocks for driving oil prices. Since shocks identified by heteroscedasticity may not be economically meaningful, we exploit the framework to test instrumental variable restrictions which are overidentifying in the heteroscedastic model. Our findings suggest that conventional supply shocks are negligible, while news shocks about future supply account for almost all the variation in oil prices.

本文提出利用随机波动率（stochastic volatility）实现结构向量自回归模型（structural vector autoregressive models，SVAR）的统计识别，并将该类模型命名为SV-SVAR。本文探讨了该模型的完全识别与部分识别方案，开发了用于极大似然推断的高效期望最大化（EM）算法。仿真实验结果表明，即便在方差过程设定有误的情形下，SV-SVAR仍能有效识别结构参数，相较其他异方差结构向量自回归模型优势尤为显著。本文将该模型应用于探究石油供给冲击对油价波动的影响权重。鉴于通过异方差性识别得到的冲击未必具备经济合理性，本文借助该框架对异方差模型中的过度识别工具变量约束开展检验。研究结果显示，传统供给冲击的影响微乎其微，而关乎未来供给的新闻冲击几乎解释了油价的全部波动。