Constructing Optimal Instruments by First-Stage Prediction Averaging
Constructing Optimal Instruments by First-Stage Prediction Averaging
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DOI：
10.3982/ecta7444
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发表时间：
2010-03
期刊：
影响因子：
6.1
通讯作者：
G. Kuersteiner;R. Okui
中科院分区：
文献类型：
--
作者：
G. Kuersteiner;R. Okui
本文认为模型平均作为一种方法来构建最佳的两阶段最小二乘（2SLS），有限信息最大似然（LIML），和富勒估计在许多工具的存在下的工具。我们建议对从许多不同的工具选择中获得的内生变量的最小二乘预测进行平均，然后在估计阶段使用内生变量的平均预测值。选择用于求平均值的权重以最小化2SLS、LIML或Fuller估计量的模型求平均值版本的渐近均方误差。这可以通过求解标准二次规划问题来完成。版权所有2010年经济计量学会。
This paper considers model averaging as a way to construct optimal instruments for the two-stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous variables obtained from many different choices of instruments and then use the average predicted value of the endogenous variables in the estimation stage. The weights for averaging are chosen to minimize the asymptotic mean squared error of the model averaging version of the 2SLS, LIML, or Fuller estimator. This can be done by solving a standard quadratic programming problem. Copyright 2010 The Econometric Society.