Partly linear instrumental variables regressions without smoothing on the instruments

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY Test Pub Date : 2024-05-30 DOI:10.1007/s11749-024-00931-z
Jean-Pierre Florens, Elia Lapenta
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Abstract

We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the Landweber–Fridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rate for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators.

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不对工具进行平滑处理的部分线性工具变量回归
我们考虑了一个由工具变量确定的半参数部分线性模型。我们提出了一种不依赖工具的估计方法,并将 Landweber-Fridman 正则化方案扩展到该半参数模型的估计中。然后,我们展示了参数估计器的渐近正态性,并获得了非参数估计器的收敛率。我们不对工具进行平滑的估计器与对工具进行平滑的典型估计器不谋而合,后者会随着样本量的增加而保持各自的带宽固定不变。我们提出了一种数据驱动的正则化参数选择方法,并在模拟研究中展示了我们的估计器极具吸引力的性能。
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来源期刊
Test
Test 数学-统计学与概率论
CiteScore
2.20
自引率
7.70%
发文量
41
审稿时长
>12 weeks
期刊介绍: TEST is an international journal of Statistics and Probability, sponsored by the Spanish Society of Statistics and Operations Research. English is the official language of the journal. The emphasis of TEST is placed on papers containing original theoretical contributions of direct or potential value in applications. In this respect, the methodological contents are considered to be crucial for the papers published in TEST, but the practical implications of the methodological aspects are also relevant. Original sound manuscripts on either well-established or emerging areas in the scope of the journal are welcome. One volume is published annually in four issues. In addition to the regular contributions, each issue of TEST contains an invited paper from a world-wide recognized outstanding statistician on an up-to-date challenging topic, including discussions.
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