摘要翻译:
本文给出了线性工具变量(IV)估计的渐近性态。通过将观测数据分解为训练样本和测试样本,经验地选择正则化调整参数。在调整参数的条件下,训练样本创建从IV估计器到先验估计器的路径。最佳调谐参数是沿着该路径使测试样本的IV目标函数最小化的值。经验选择的正则化调谐参数成为与感兴趣参数联合收敛的估计参数。调谐参数的渐近分布是非标准混合分布。Monte Carlo仿真表明,渐近分布捕捉到了抽样分布的特征,当这种岭估计比两级最小二乘估计性能更好时。
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英文标题:
《The Ridge Path Estimator for Linear Instrumental Variables》
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作者:
Nandana Sengupta and Fallaw Sowell
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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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一级分类:Statistics 统计学
二级分类:Machine Learning 机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
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英文摘要:
This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates a path from the IV estimator to a prior. The optimal tuning parameter is the value along this path that minimizes the IV objective function for the test sample. The empirically selected regularization tuning parameter becomes an estimated parameter that jointly converges with the parameters of interest. The asymptotic distribution of the tuning parameter is a nonstandard mixture distribution. Monte Carlo simulations show the asymptotic distribution captures the characteristics of the sampling distributions and when this ridge estimator performs better than two-stage least squares.
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PDF链接:
https://arxiv.org/pdf/1908.09237