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. 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. The introduced methods are implemented to the accompanying R package sstvars.

翻译：线性结构向量自回归模型在冲击相互独立且最多只有一个冲击服从高斯分布的情况下，无需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和平滑转换向量自回归模型，这类模型包含一个时变影响矩阵，定义为各机制影响矩阵的加权和。我们的实证应用研究了气候政策不确定性冲击对美国宏观经济的影响。在一个包含两个机制的结构逻辑平滑转换向量自回归模型中，我们发现：正向气候政策不确定性冲击在低经济政策不确定性时期会降低产出，但在高经济政策不确定性时期则会略微提升产出。所介绍的方法已在配套R包sstvars中实现。