Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Reply
回应Kranz和Pütz的批评，验证了工具变量和双重差分文章比随机实验和断点回归更易被p值操纵，但修正舍入误差后，双重差分的p值聚集现象减弱。
In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial and regression discontinuity design articles. We also find no evidence that (i) articles published in the top five journals are different; (ii) the “revise and resubmit” process mitigates the problem; (iii) things are improving through time. Kranz and Pütz (2022) apply a novel adjustment to address rounding errors. They successfully replicate our results with the exception of our shakiest finding: after adjusting for rounding errors, bunching of test statistics for difference-in-difference articles is now smaller around the 5 percent level (and coincidentally larger at the 10 percent level).