Wild bootstrap for instrumental variable regressions with weak and few clusters

We study the wild bootstrap inference for instrumental variable (quantile) regressions in the framework of a small number of large clusters, in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. For subvector inference, we show that...

Full description

Saved in:
Bibliographic Details
Main Authors: WANG, Wenjie, ZHANG, Yichong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Online Access:https://ink.library.smu.edu.sg/soe_research/2497
https://ink.library.smu.edu.sg/context/soe_research/article/3496/viewcontent/wild_bootstrap.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soe_research-3496
record_format dspace
spelling sg-smu-ink.soe_research-34962021-10-14T05:47:09Z Wild bootstrap for instrumental variable regressions with weak and few clusters WANG, Wenjie ZHANG, Yichong We study the wild bootstrap inference for instrumental variable (quantile) regressions in the framework of a small number of large clusters, in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. For subvector inference, we show that the wild bootstrap Wald test with or without using the cluster-robust covariance matrix controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. We further develop a wild bootstrap Anderson-Rubin (AR) test for full-vector inference and show that it controls size asymptotically up to a small error even under weak or partial identification for all clusters. We illustrate the good finite-sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about U.S. local labor markets. 2021-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2497 https://ink.library.smu.edu.sg/context/soe_research/article/3496/viewcontent/wild_bootstrap.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Wild Bootstrap Weak Instrument Clustered Data Randomization Test InstrumentalVariable Quantile Regression Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Wild Bootstrap
Weak Instrument
Clustered Data
Randomization Test
InstrumentalVariable Quantile Regression
Econometrics
spellingShingle Wild Bootstrap
Weak Instrument
Clustered Data
Randomization Test
InstrumentalVariable Quantile Regression
Econometrics
WANG, Wenjie
ZHANG, Yichong
Wild bootstrap for instrumental variable regressions with weak and few clusters
description We study the wild bootstrap inference for instrumental variable (quantile) regressions in the framework of a small number of large clusters, in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. For subvector inference, we show that the wild bootstrap Wald test with or without using the cluster-robust covariance matrix controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. We further develop a wild bootstrap Anderson-Rubin (AR) test for full-vector inference and show that it controls size asymptotically up to a small error even under weak or partial identification for all clusters. We illustrate the good finite-sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about U.S. local labor markets.
format text
author WANG, Wenjie
ZHANG, Yichong
author_facet WANG, Wenjie
ZHANG, Yichong
author_sort WANG, Wenjie
title Wild bootstrap for instrumental variable regressions with weak and few clusters
title_short Wild bootstrap for instrumental variable regressions with weak and few clusters
title_full Wild bootstrap for instrumental variable regressions with weak and few clusters
title_fullStr Wild bootstrap for instrumental variable regressions with weak and few clusters
title_full_unstemmed Wild bootstrap for instrumental variable regressions with weak and few clusters
title_sort wild bootstrap for instrumental variable regressions with weak and few clusters
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/soe_research/2497
https://ink.library.smu.edu.sg/context/soe_research/article/3496/viewcontent/wild_bootstrap.pdf
_version_ 1770575901328670720