Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters
Bibliographic Data
| ID | 19418196 |
|---|---|
| Authors | Wenjie Wang (0000-0002-3103-8445, Nanyang Technological University), Yichong Zhang (0000-0002-0677-7203, Singapore Management University, corresponding author) |
| Year | 2026 |
| Pages | 1-12 |
| Publication date | 2026-04-27 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2026.2618311 |
| OpenAlex | W7125971995 |
| Language | EN |
| References cited | 59 |
We study the gradient wild bootstrap-based 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. 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 US local labor markets
Feature selection · Instrumental variable · Quantile · Quantile regression · Bayesian Methods and Mixture Models · Statistical Methods and Inference · Stochastic Gradient Optimization Techniques
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| Highly cited | No |
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