Quantile‐Based Test for Heterogeneous Treatment Effects
Bibliographic Data
| ID | 21653252 |
|---|---|
| Authors | EunYi Chung (Department of Economics University of Illinois, Urbana–Champaign Urbana Illinois USA, corresponding author), Mauricio Olivares (0000-0003-3373-8629, Department of Statistics LMU Munich Munich Germany) |
| Year | 2025 |
| Volume | 40 |
| Issue | 1 |
| Pages | 3-17 |
| Publication date | 2025-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3093 |
| OpenAlex | W4403500482 |
| Language | EN |
| References cited | 35 |
We introduce a permutation test for heterogeneous treatment effects based on the quantile process. However, tests based on the quantile process often suffer from estimated nuisance parameters that jeopardize their validity, even in large samples. To overcome this problem, we use Khmaladze's martingale transformation. We show that the permutation test based on the transformed statistic controls size asymptotically. Numerical evidence asserts the good size and power performance of our test procedure compared to other popular quantile‐based tests. We discuss a fast implementation algorithm and illustrate our method using experimental data from a welfare reform
Econometrics · Economics · Quantile · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Statistical Methods and Inference · Statistical Methods in Clinical Trials · Geology
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| Highly cited | No |
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