Economics at your fingertips  

Robust uniform inference for quantile treatment effects in regression discontinuity designs

Harold D. Chiang, Yu-Chin Hsu () and Yuya Sasaki

Journal of Econometrics, 2019, vol. 211, issue 2, 589-618

Abstract: The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust methods cover many cases, but do not handle uniform inference for CDF and quantile processes in fuzzy designs. In this light, this paper extends the literature by developing a unified framework of inference with robustness against large bandwidths that applies to uniform inference for quantile treatment effects in fuzzy designs, as well as all the other cases. We present Monte Carlo simulation studies and an empirical application for evaluations of the Oklahoma pre-K program.

Keywords: Bias correction; Local Wald estimator; Multiplier bootstrap; Quantile; Regression discontinuity design; Regression kink design; Robustness (search for similar items in EconPapers)
JEL-codes: C01 C14 C21 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.jeconom.2019.03.006

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-08-23
Handle: RePEc:eee:econom:v:211:y:2019:i:2:p:589-618