Alexander Kreiß, Christoph Rothe
arXiv 26 Oct 2021 · Econometrics · publishedEconometrics Journal (2022) · 4 citations (OpenAlex)
arXiv:2110.13725 · PDF · DOI · OpenAlex · Extracted main text
We study regression discontinuity designs in which many predetermined covariates, possibly much more than the number of observations, can be used to increase the precision of treatment effect estimates. We consider a two-step estimator which first selects a small number of "important" covariates through a localized Lasso-type procedure, and then, in a second step, estimates the treatment effect by including the selected covariates linearly into the usual local linear estimator. We provide an in-depth analysis of the algorithm's theoretical properties, showing that, under an approximate sparsity condition, the resulting estimator is asymptotically normal, with asymptotic bias and variance that are conceptually similar to those obtained in low-dimensional settings. Bandwidth selection and inference can be carried out using standard methods. We also provide simulations and an empirical application.
appendix boundary found by appendix_command · 33% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Armstrong, T. B. and M. Kolesár (2018) Optimal inference in a class of regression models | 1.000 | 9 | 3 | 100% |
| 2 | Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik (2019) Regression discontinuity designs using covariates | 0.950 | 14 | 5 | 86% |
| 3 | Belloni, A., V. Chernozhukov, and C. Hansen (2013) Inference on treatment effects after selection among high-dimensional controls | 0.920 | 9 | 5 | 78% |
| 4 | Imbens, G. and K. Kalyanaraman (2012) Optimal bandwidth choice for the regression discontinuity estimator | 0.843 | 3 | 3 | 100% |
| 5 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.843 | 3 | 3 | 100% |
| 6 | van de Geer, S. and P. Bühlmann (2011) Statistics for high-dimensional data | 0.693 | 8 | 2 | 50% |
| 7 | Arai, Y., T. Otsu, and M. H. Seo (2022) Regression Discontinuity Design with Potentially Many Covariates | 0.644 | 4 | 1 | 100% |
| 8 | Card, D., R. Chetty, and A. Weber (2007) Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market | 0.585 | 3 | 1 | 100% |
| 9 | Belloni, A. and V. Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models | 0.511 | 10 | 2 | 20% |
| 10 | Lederer, J. and M. Vogt (2021) Estimating the Lasso's Effective Noise | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs | 0.405 | 1 | 1 |
| 2 | Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing | 0.405 | 1 | 1 |