关于机器学习工具变量估计

IF 1.8 4区 经济学 Q2 ECONOMICS
Edvard Bakhitov
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引用次数: 0

摘要

本文探讨了非参数工具变量(NPIV)估计问题的病态性所带来的实际挑战。我们表明,即使在“中等”维度下,传统的NPIV序列估计器也难以以所需的精度估计潜在的结构函数。我们认为机器学习工具变量算法利用复杂的正则化技术来缓解这些问题,实现卓越的有限样本性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On machine learning instrumental variable estimators
This paper examines the practical challenges arising from the ill-posedness of the nonparametric instrumental variable (NPIV) estimation problem. We show that conventional NPIV series estimators struggle to estimate the underlying structural function with desired precision even in “moderate” dimensions. We argue that machine learning instrumental variable algorithms leverage sophisticated regularization techniques to mitigate these issues, achieving superior finite-sample performance.
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来源期刊
Economics Letters
Economics Letters ECONOMICS-
CiteScore
3.20
自引率
5.00%
发文量
348
审稿时长
30 days
期刊介绍: Many economists today are concerned by the proliferation of journals and the concomitant labyrinth of research to be conquered in order to reach the specific information they require. To combat this tendency, Economics Letters has been conceived and designed outside the realm of the traditional economics journal. As a Letters Journal, it consists of concise communications (letters) that provide a means of rapid and efficient dissemination of new results, models and methods in all fields of economic research.
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