Minimum Contrast Empirical Likelihood Inference of Discontinuity in Density
Bibliographic Data
| ID | 19418277 |
|---|---|
| Authors | Jun Ma (0000-0003-3412-7766, Renmin University of China), Hugo Jales (0000-0001-7718-0132, Syracuse University), Zhengfei Yu (0000-0002-5987-4875, University of Tsukuba) |
| Year | 2020 |
| Volume | 38 |
| Issue | 4 |
| Pages | 934-950 |
| Publication date | 2020-10-01 |
| 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 | 10.1080/07350015.2019.1617155 |
| OpenAlex | W2945489780 |
| Language | EN |
| Citations received | 1 |
| References cited | 34 |
This article investigates the asymptotic properties of a simple empirical-likelihood-based inference method for discontinuity in density. The parameter of interest is a function of two one-sided limits of the probability density function at (possibly) two cut-off points. Our approach is based on the first-order conditions from a minimum contrast problem. We investigate both first-order and second-order properties of the proposed method. We characterize the leading coverage error of our inference method and propose a coverage-error-optimal (CE-optimal, hereafter) bandwidth selector. We show that the empirical likelihood ratio statistic is Bartlett correctable. An important special case is the manipulation testing problem in a regression discontinuity design (RDD), where the parameter of interest is the density difference at a known threshold. In RDD, the continuity of the density of the assignment variable at the threshold is considered as a “no-manipulation” behavioral assumption, which is a testable implication of an identifying condition for the local average treatment effect. When specialized to the manipulation testing problem, the CE-optimal bandwidth selector has an explicit form. We propose a data-driven CE-optimal bandwidth selector for use in practice. Results from Monte Carlo simulations are presented. Usefulness of our method is illustrated by an empirical example
Causal inference · Density contrast · Econometrics · Economics · Inference · Physics · Statistics · Computer Science · Mathematics · Probabilistic and Robust Engineering Design · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
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| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |