Identification of vector autoregressive models with nonlinear contemporaneous structure
提出了一种识别递归结构向量自回归模型的方法，该模型在同期层面存在非线性依赖，可用于估计非线性结构脉冲响应函数，并通过模拟和宏观经济冲击研究验证了其有效性。
We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models, we show that, under certain conditions, a large class of structural VAR models is identifiable. We spell out these specific conditions and propose a scheme for the estimation of structural impulse response functions in a nonlinear setting. We assess the performance of this scheme in a simulation experiment. Finally, we apply it in a study on the effects of the macroeconomic shocks that propagate through the economy, allowing for asymmetry between responses from positive and negative impulses.