具有参数横截面依赖性的非参数面板数据回归

IF 2.9 4区经济学 Q1 ECONOMICS

Econometrics Journal Pub Date : 2021-05-08 DOI:10.1093/ECTJ/UTAB016

A. Soberón, Juan M. Rodríguez-Póo, P. Robinson

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引用次数: 0

摘要

在本文中，我们考虑了具有横截面相关性的非参数面板数据模型的效率改进。考虑到这种依赖结构，提出了一种广义最小二乘估计器。将横截面相关性参数化，证明了局部线性估计量由这种类型的GLS估计量主导。此外，还研究了在收敛速度方面可能获得的增益。渐近最优带宽选择是合理的。为了评估所提出的估计器的有限样本性能，进行了蒙特卡洛研究。此外，还进行了一些实证应用，目的是分析欧洲货币联盟对其成员国的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nonparametric panel data regression with parametric cross-sectional dependence

In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A generalised least squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterising the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analysing the implications of the European Monetary Union for its member countries.

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来源期刊

Econometrics Journal 管理科学-数学跨学科应用

CiteScore

4.20

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： The Econometrics Journal was established in 1998 by the Royal Economic Society with the aim of creating a top international field journal for the publication of econometric research with a standard of intellectual rigour and academic standing similar to those of the pre-existing top field journals in econometrics. The Econometrics Journal is committed to publishing first-class papers in macro-, micro- and financial econometrics. It is a general journal for econometric research open to all areas of econometrics, whether applied, computational, methodological or theoretical contributions.