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 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.

中文翻译：

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非高斯线性联立方程模型的局部鲁棒推理

如果潜在的结构冲击是独立的并且最多其中一个是高斯分布的，则线性联立方程模型中的所有参数都可以被识别（直到排列和符号）。不幸的是，当冲击的真实分布接近高斯时，利用此类识别假设的现有推理方法会遭受尺寸扭曲。为了解决这个问题，我们开发了一种局部鲁棒的半参数推理方法，该方法易于实现，提高了覆盖范围并保留了良好的功率特性。该方法的有限样本特性在大型模拟研究和学校教育回报实证研究中得到了说明。