Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models
提出一种新的广义预测误差方差分解方法，确保各变量创新贡献比例之和为1，基于广义脉冲响应函数，可通过模拟轻松实现，并用美国产出增长与利差数据展示应用。
Abstract We propose a new generalized forecast error variance decomposition with the attractive property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the generalized impulse response function, and it can easily be obtained by simulation. The new decomposition is illustrated in an empirical application to US output growth and interest rate spread data.