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 labelling of the shocks, maximum likelihood 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 in times of low economic policy uncertainty but slightly increases it in times 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，则无需对模型施加限制即可实现线性结构向量自回归模型的统计识别。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，此类模型通过将各体制冲击矩阵加权求和的方式构建时变冲击矩阵。文中同时探讨了冲击项的标记问题、参数的最大似然估计以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。在关于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，基于双体制结构逻辑平滑转移向量自回归模型的分析表明：气候政策不确定性正向冲击在经济政策不确定性较低时期会抑制产出，而在经济政策不确定性较高时期则会轻微促进产出。