Wild bootstrap inference for instrumental variables regressions with weak and few clusters
We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild b...
Saved in:
Main Authors: | WANG, Wenjie, ZHANG, Yichong |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/soe_research/2741 https://ink.library.smu.edu.sg/context/soe_research/article/3740/viewcontent/Wildbootstrap_clusters_sv.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Wild bootstrap for instrumental variable regressions with weak and few clusters
by: WANG, Wenjie, et al.
Published: (2021) -
Quantile treatment effects and bootstrap inference under covariate-adaptive randomization
by: ZHENG, Xin, et al.
Published: (2018) -
Quantile treatment effects and bootstrap inference under covariate-adaptive randomization
by: ZHANG, Yichong, et al.
Published: (2020) -
Bootstrap inference for quantile treatment effects in randomized experiments with matched pairs
by: JIANG, Liang, et al.
Published: (2024) -
Bootstrap LM tests for higher-order spatial effects in spatial linear regression models
by: YANG, Zhenlin
Published: (2018)