PLRD: Partially Linear Regression Discontinuity Inference
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911292313305088 |
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| author | Ghosh, Aditya Imbens, Guido Wager, Stefan |
| author_facet | Ghosh, Aditya Imbens, Guido Wager, Stefan |
| contents | Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PLRD: Partially Linear Regression Discontinuity Inference Ghosh, Aditya Imbens, Guido Wager, Stefan Econometrics Methodology Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions. |
| title | PLRD: Partially Linear Regression Discontinuity Inference |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2503.09907 |