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 with 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：若冲击项相互独立且至多一项服从高斯分布，则线性结构向量自回归模型无需施加模型约束即可实现统计识别。本文证明该结论可推广至结构阈值与平滑转移向量自回归模型，此类模型通过制度区间影响矩阵的加权和定义时变影响矩阵。我们同时讨论了冲击标记、参数估计及模型平稳性问题。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研究中，基于双制度区间的结构逻辑平滑转移向量自回归模型发现：正气候政策不确定性冲击在经济政策不确定性较低与较高时期均会抑制产出并推升通胀，但其通胀效应在经济政策不确定性较高时期更为显著。