This thesis studies regression discontinuity designs with the use of additional covariates for estimation of the average treatment effect. We prove asymptotic normality of the covariate-adjusted estimator under sufficient regularity conditions. In the case of a high-dimensional setting with a large number of covariates depending on the number of observations, we discuss a Lasso-based selection approach as well as alternatives based on calculated correlation thresholds. We present simulation results on those alternative selection strategies.:1. Introduction
2. Preliminaries
3. Regression Discontinuity Designs
4. Setup and Notation
5. Computing the Bias
6. Asymptotic Behavior
7. Asymptotic Normality of the Estimator
8. Including Potentially Many Covariates
9. Simulations
10. Conclusion
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87909 |
| Date | 07 November 2023 |
| Creators | Kramer, Patrick |
| Contributors | Kreiß, Alexander, Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0019 seconds