论文标题
快速，强大的线性仪器变量模型使用自相应力矩
Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments
论文作者
论文摘要
我们提出并实施了一种在线性仪器变量模型中推断的方法，该模型同时稳健且可在计算上进行。推论是基于样本矩条件的自称，并允许（但不需要）许多（相对于样本量），弱，潜在的或潜在的内源性仪器，以及许多回归器和条件异质性。我们的覆盖范围是统一的，可以提供少量的样本保证。我们基于半决赛编程开发了一种新的计算方法，我们表明，它可以同样应用于快速反转现有测试（例如，AR，LM，CLR等）。
We propose and implement an approach to inference in linear instrumental variables models which is simultaneously robust and computationally tractable. Inference is based on self-normalization of sample moment conditions, and allows for (but does not require) many (relative to the sample size), weak, potentially invalid or potentially endogenous instruments, as well as for many regressors and conditional heteroskedasticity. Our coverage results are uniform and can deliver a small sample guarantee. We develop a new computational approach based on semidefinite programming, which we show can equally be applied to rapidly invert existing tests (e.g,. AR, LM, CLR, etc.).
