带有变量误差的非参数回归的带宽选择

IF 0.8 4区 经济学 Q3 ECONOMICS Econometric Reviews Pub Date : 2023-04-21 DOI:10.1080/07474938.2023.2191105
Hao Dong, Taisuke Otsu, L. Taylor
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引用次数: 2

摘要

摘要针对回归量中存在经典测量误差的非参数回归模型,提出了两种新的带宽选择方法。每种方法都使用第二次(密度)反卷积来评估回归的预测误差。第一种方法使用典型的leave-one-out交叉验证标准,而第二种方法应用自举方法和out- bag预测的概念。我们证明了这两种方法的渐近有效性,并将它们与蒙特卡洛研究中的SIMEX方法进行了比较。除了显著降低计算成本外,本文中提出的方法与当前最先进的方法相比,具有更低的平均积分平方误差(MISE)。
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Bandwidth selection for nonparametric regression with errors-in-variables
Abstract We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.
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来源期刊
Econometric Reviews
Econometric Reviews 管理科学-数学跨学科应用
CiteScore
1.70
自引率
0.00%
发文量
27
审稿时长
>12 weeks
期刊介绍: Econometric Reviews is widely regarded as one of the top 5 core journals in econometrics. It probes the limits of econometric knowledge, featuring regular, state-of-the-art single blind refereed articles and book reviews. ER has been consistently the leader and innovator in its acclaimed retrospective and critical surveys and interchanges on current or developing topics. Special issues of the journal are developed by a world-renowned editorial board. These bring together leading experts from econometrics and beyond. Reviews of books and software are also within the scope of the journal. Its content is expressly intended to reach beyond econometrics and advanced empirical economics, to statistics and other social sciences.
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