Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/180578 
Year of Publication: 
2018
Series/Report no.: 
IZA Discussion Papers No. 11560
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
In regression discontinuity design (RD), for a given bandwidth, researchers can estimate standard errors based on different variance formulas obtained under different asymptotic frameworks. In the traditional approach the bandwidth shrinks to zero as sample size increases; alternatively, the bandwidth could be treated as fixed. The main theoretical results for RD rely on the former, while most applications in the literature treat the estimates as parametric, implementing the usual heteroskedasticity-robust standard errors. This paper develops the "fixed-bandwidth" alternative asymptotic theory for RD designs, which sheds light on the connection between both approaches. I provide alternative formulas (approximations) for the bias and variance of common RD estimators, and conditions under which both approximations are equivalent. Simulations document the improvements in test coverage that fixed-bandwidth approximations achieve relative to traditional approximations, especially when there is local heteroskedasticity. Feasible estimators of fixed-bandwidth standard errors are easy to implement and are akin to treating RD estimators as locally parametric, validating the common empirical practice of using heteroskedasticity-robust standard errors in RD settings. Bias mitigation approaches are discussed and a novel, bootstrap higher-order bias correction procedure based on the fixed bandwidth asymptotics is suggested.
Subjects: 
average treatment effect
locally parametric inference
local polynomial estimators
fixed bandwidth
heteroskedasticity robust standard errors
bias correction
JEL: 
C12
C21
Document Type: 
Working Paper

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