Locally robust inference for non-Gaussian linear simultaneous equations models
针对结构冲击接近高斯分布时现有推断方法存在尺寸扭曲的问题，提出一种局部稳健的半参数推断方法，易于实施、改善覆盖并保持良好功效，通过模拟和实证研究验证了有限样本性质。
All parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distortions when the true distributions of the shocks are close to Gaussian. To address this weak non-Gaussian problem we develop a locally robust semi-parametric inference method which is simple to implement, improves coverage and retains good power properties. The finite sample properties of the methodology are illustrated in a large simulation study and an empirical study for the returns to schooling.