摘要翻译：
为了提高工具变量分位数回归(IVQR)估计的有限样本效率，提出了平均估计方法。首先，我将Cheng，Liao，Shi(2019)的平均GMM框架应用到IVQR模型中。我建议使用通常的分位数回归矩进行平均，以利用内生性不太强的情况。我还建议使用两级最小二乘斜率矩来利用异质性不太强的情况。程等人的经验最优权重公式。（2019）有助于优化偏差-方差权衡，确保在特定条件下平均估计器比标准IVQR估计器具有一致更好的（渐近）风险。我的实现涉及许多计算方面的考虑，并基于分位数文献的最新发展。其次，我提出了一种直接平均IVQR、分位数回归和两阶段最小二乘估计的bootstrap方法。更具体地说，我在bootstrap世界中找到最优权重，然后将bootstrap最优权重应用到原始样本。bootstrap方法计算简单，一般在仿真中表现较好，但缺乏Cheng等人的形式一致优势结果。（2019年）。仿真结果表明，在多个回归子/仪器的情况下，GMM平均估计和bootstrap估计在不同内生性水平和异质性水平的组合下，在数据生成过程(DGPs)上均比IVQR估计具有更小的风险。在具有单一内生回归和工具的差分全球定位系统中，平均估计的改进机会最少，所提出的平均估计在某些情况下优于IVQR估计，但在其他情况下不是。
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英文标题：
《Averaging estimation for instrumental variables quantile regression》
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作者：
Xin Liu
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper proposes averaging estimation methods to improve the finite-sample efficiency of the instrumental variables quantile regression (IVQR) estimation. First, I apply Cheng, Liao, Shi's (2019) averaging GMM framework to the IVQR model. I propose using the usual quantile regression moments for averaging to take advantage of cases when endogeneity is not too strong. I also propose using two-stage least squares slope moments to take advantage of cases when heterogeneity is not too strong. The empirical optimal weight formula of Cheng et al. (2019) helps optimize the bias-variance tradeoff, ensuring uniformly better (asymptotic) risk of the averaging estimator over the standard IVQR estimator under certain conditions. My implementation involves many computational considerations and builds on recent developments in the quantile literature. Second, I propose a bootstrap method that directly averages among IVQR, quantile regression, and two-stage least squares estimators. More specifically, I find the optimal weights in the bootstrap world and then apply the bootstrap-optimal weights to the original sample. The bootstrap method is simpler to compute and generally performs better in simulations, but it lacks the formal uniform dominance results of Cheng et al. (2019). Simulation results demonstrate that in the multiple-regressors/instruments case, both the GMM averaging and bootstrap estimators have uniformly smaller risk than the IVQR estimator across data-generating processes (DGPs) with all kinds of combinations of different endogeneity levels and heterogeneity levels. In DGPs with a single endogenous regressor and instrument, where averaging estimation is known to have least opportunity for improvement, the proposed averaging estimators outperform the IVQR estimator in some cases but not others.
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PDF链接：
https://arxiv.org/pdf/1910.04245