Identification of Structural Vector Autoregressions by Stochastic Volatility
提出利用随机波动来统计识别结构向量自回归模型，开发了高效EM算法，并应用于研究石油供给冲击对油价的影响，发现传统供给冲击影响很小，而未来供给的新闻冲击几乎解释了油价全部波动。
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.