Linear structural vector autoregressive models can be identified statistically without imposing restrictions on the model if the shocks are mutually independent and at most one of them is Gaussian. We show that this result extends to structural threshold and smooth transition vector autoregressive models incorporating a time-varying impact matrix defined as a weighted sum of the impact matrices of the regimes. We also discuss the problem of labelling the shocks, estimation of the parameters, and stationarity the model. The introduced methods are implemented to the accompanying R package sstvars. Our empirical application studies the effects of the climate policy uncertainty shock on the U.S. macroeconomy. In a structural logistic smooth transition vector autoregressive model consisting of two regimes, we find that a positive climate policy uncertainty shock decreases production and increases inflation in times of both low and high economic policy uncertainty, but its inflationary effects are stronger in the periods of high economic policy uncertainty.

翻译：在线性结构向量自回归模型中，若冲击相互独立且至多一个服从高斯分布，则无需对模型施加限制即可实现统计识别。本文证明该结论可推广至结构阈值与平滑转换向量自回归模型，此类模型通过将各状态区制的影响矩阵加权求和来定义时变影响矩阵。我们同时讨论了冲击标记问题、参数估计方法及模型的平稳性条件。所提出的方法已在配套R包sstvars中实现。在针对气候政策不确定性冲击对美国宏观经济影响的实证应用中，我们构建了一个包含两个状态区制的结构逻辑平滑转换向量自回归模型，发现无论是在经济政策不确定性较低还是较高时期，正向气候政策不确定性冲击均会导致产出下降与通胀上升，但其通胀效应在经济政策不确定性较高时期更为显著。