Three Essays in Double/debiased Machine Learning and High-dimensional Econometrics

In today’s big data world, we have witnessed rapidly increasing popularity of machine learning methods in empirical studies, such as random forests, lasso, post-lasso, elastic nets, ridge, deep neural networks, and boosted trees among others. The objective of this paper is motivated by recently incr...

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Bibliographic Details
Main Author: Ma, Yukun
Other Authors: Sasaki, Yuya
Format: Thesis
Language:English
Published: 2024
Subjects:
Weak identification
local average treatment effect
double/debiased machine learning
multiway cross fitting
dyadic cross fitting
DML
Online Access:http://hdl.handle.net/1803/18965
Description
Summary:In today’s big data world, we have witnessed rapidly increasing popularity of machine learning methods in empirical studies, such as random forests, lasso, post-lasso, elastic nets, ridge, deep neural networks, and boosted trees among others. The objective of this paper is motivated by recently increasing demand for Dou- ble/debiased Machine Learning (DML) methods in empirical research.

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