Regression discontinuity design with unknown cutoff: cutoff detection & effect estimation

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dc.contributor.authorKhan Tanu, Tanvir Ahmed
dc.contributor.supervisorPretis, Felix
dc.date.accessioned2020-08-27T23:27:17Z
dc.date.available2020-08-27T23:27:17Z
dc.date.copyright2020en_US
dc.date.issued2020-08-27
dc.degree.departmentDepartment of Economicsen_US
dc.degree.levelMaster of Arts M.A.en_US
dc.description.abstractRegression discontinuity designs are increasingly popular quasi-experimental research designs among applied econometricians desiring to make causal inferences on the local effect of a treatment, intervention, or policy. They are also widely used in social, behavioral, and natural sciences. Much of the existing literature relies on the assumption that the discontinuity point or cutoff is known a-priori, which may not always hold. This thesis seeks to extend the applicability of regression discontinuity designs by proposing a new approach towards detection of an unknown discontinuity point using structural-break detection and machine learning methods. The approach is evaluated on both simulated and real data. Estimation and inference based on estimating the cutoff following this approach are compared to the counterfactual scenario where the cutoff is known. Monte Carlo simulations show that the empirical false-detection and true-detection probabilities of the proposed procedure are generally satisfactory. Finally, the approach is further illustrated with an empirical application.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/12034
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectRegression Discontinuity Designen_US
dc.subjectRDDen_US
dc.subjectApplied Econometricsen_US
dc.subjectEconometricsen_US
dc.subjectTime Seriesen_US
dc.subjectStructural Break Detectionen_US
dc.subjectTipping Point Detectionen_US
dc.subjectRDDsen_US
dc.subjectRKDen_US
dc.subjectRKDsen_US
dc.subjectRegression Discontinuity Designsen_US
dc.subjectMachine Learningen_US
dc.titleRegression discontinuity design with unknown cutoff: cutoff detection & effect estimationen_US
dc.typeThesisen_US

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