用惩罚经验似然剔除矩条件中的“害群之马”
Culling the herd of moments with penalized empirical likelihood
AI总结：
针对高维矩条件模型，提出惩罚经验似然估计，能一致识别无效矩，具有预言性质，并通过投影修正偏差，提升有限样本推断精度。
摘要：
由矩条件定义的模型是结构计量经济估计的核心，但经济理论对矩选择大多持不可知态度。虽然大量有效矩可能潜在地提高估计效率，但与此同时，少数无效矩可能破坏一致性。本文研究高维设置下这些矩定义模型的经验似然估计。我们提出一种惩罚经验似然（PEL）估计，并建立其具有一致检测无效矩的预言性质。PEL估计量渐近正态分布，且投影PEL程序进一步消除其渐近偏差，并为有限样本行为提供更准确的正态近似。模拟实验表明这些方法在估计和推断方面具有优异的数值性能。
英文摘要：
Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection. While a large pool of valid moments can potentially improve estimation efficiency, in the meantime a few invalid ones may undermine consistency. This paper investigates the empirical likelihood estimation of these moment-defined models in high-dimensional settings. We propose a penalized empirical likelihood (PEL) estimation and establish its oracle property with consistent detection of invalid moments. The PEL estimator is asymptotically normally distributed, and a projected PEL procedure further eliminates its asymptotic bias and provides more accurate normal approximation to the finite sample behavior. Simulation exercises demonstrate excellent numerical performance of these methods in estimation and inference.
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