Uncertain Short‐Run Restrictions and Statistically Identified Structural Vector Autoregressions
提出一种结合统计识别与可能无效的短期零约束的方法，通过收缩估计提升效率，并应用于石油市场模型，发现股票市场数据对识别信息冲击至关重要。
ABSTRACT This study proposes a combination of a statistical identification approach with potentially invalid short‐run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. We demonstrate that incorporating valid restrictions through the shrinkage approach enhances the efficiency of the statistically identified estimator, and the impact of invalid restrictions vanishes as the sample size increases. Applying the estimator to an oil market model indicates that incorporating stock market data into the analysis is crucial, as it enables the identification of information shocks, which are shown to be important drivers of the oil price.