A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments

I propose a generalized method of moments estimator for structural vector autoregressions with independent and non-Gaussian shocks. The shocks are identified by exploiting information contained in higher moments of the data. Extending the standard identification approach, which relies on the covariance, to the coskewness and cokurtosis allows the simultaneous interaction to be identified and estimated without any further restrictions. I analyze the finite sample properties of the estimator and apply it to illustrate the simultaneous interaction between economic activity, oil, and stock prices. Supplementary materials for this article are available online.

本文提出一种针对带有独立非高斯冲击的结构向量自回归（Structural Vector Autoregression, SVAR）模型的广义矩估计（Generalized Method of Moments, GMM）量。该冲击通过利用数据高阶矩所蕴含的信息实现识别。将依赖协方差的标准识别方法拓展至协偏度（coskewness）与协峰度（cokurtosis）领域，可在无需额外约束的前提下，实现对同期交互效应的识别与估计。本文分析了该估计量的有限样本性质，并将其应用于阐释经济活动、石油市场与股票价格之间的同期交互关系。本文的补充材料可在线获取。