AI 中文总结
该研究提出一种通过独立创新分析识别的完全非线性结构向量自回归模型，采用对比学习与指数族规范，结合前馈神经网络估计，实证发现美国工业产出对油价冲击的响应存在适度非对称性，并开发了R包iiasvar实现方法。
AI 中文摘要
我们开发了一种完全非线性的结构向量自回归框架，其中同期结构映射可以是非线性且非加性的。识别是通过利用观测到的外生变量所诱导的相互独立的结构冲击的条件分布变化来实现的。具体而言，我们采用了一种通用的对比学习框架，该框架利用这种变化以及假设的指数族结构来恢复这些冲击。现有的独立创新分析结果仅能将此类冲击识别到任意分量可逆变换的程度，这对于结构计量经济学分析通常是不够的。我们通过对冲击的条件分布施加结构化的指数族规范来强化这一结果。在施加充分统计量后，剩余的模糊性被简化为每个冲击的单参数变换尺度映射。接着我们证明，在用于自然参数的逻辑斯特规范下，识别会进一步强化到置换和分量符号变化的程度。一旦冲击被恢复，完全非线性结构向量自回归可利用前馈神经网络进行估计，这得益于其通用近似能力。实证应用研究了美国工业产出对实际油价冲击的响应中的非对称性，我们发现冲击符号和经济状态方面存在适度的非对称性。附带的R包iiasvar实现了所提出的方法。
英文摘要
We develop a fully nonlinear structural vector autoregressive framework in which the contemporaneous structural mapping may be nonlinear and non-additive. Identification is achieved by exploiting variation in the conditional distributions of the mutually independent structural shocks induced by an observed exogenous variable. Specifically, a general contrastive learning framework that makes use of this variation together with the assumed exponential-family structure is employed to recover the shocks. Existing independent innovation analysis results identify such shocks only up to arbitrary componentwise invertible transformations, which is generally insufficient for structural econometric analysis. We strengthen this result by imposing a structured exponential-family specification for the conditional shock distributions. With the imposed sufficient statistics, the remaining ambiguity is reduced to a one-parameter transformed-scale map for each shock. We then show that, under a logistic specification used for the natural parameters, the identification is further strengthened up to permutation and componentwise sign changes. Once the shocks have been recovered, the fully nonlinear structural vector autoregression can be estimated using feed-forward neural networks, motivated by their universal approximation capabilities. The empirical application studies asymmetries in the responses of U.S. industrial production to the real oil price shock. We find modest asymmetries with respect to the sign of the shock and state of the economy. The accompanying R package iiasvar implements the introduced methods.
