Litcius/Paper detail

Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Comment

Sebastian Kranz, Peter Pütz

2022American Economic Review31 citationsDOI

Abstract

Brodeur, Cook, and Heyes (2020) study hypothesis tests from economic articles and find evidence for p-hacking and publication bias, in particular for instrumental variable and difference-in-difference studies. When adjusting for rounding errors (introducing a novel method), statistical evidence for p-hacking from randomization tests and caliper tests at the 5 percent significance threshold vanishes for difference-in-differnce studies but remains for instrumental variable studies. Results at the 1 percent and 10 percent significance thresholds remain largely similar. In addition, Brodeur, Cook, and Heyes derive latent distributions of z-statistics absent publication bias using two different approaches. We establish for each approach a result that challenges its applicability. (JEL A14, C12, C52)

Topics & Concepts

HackerInstrumental variableEconometricsStatistical hypothesis testingRoundingOmitted-variable biasStatisticsEconomicsPublication biasNull hypothesisCausal inferenceSignificance testingFrequentist inferencePsychologyPositive economicsMathematicsComputer scienceBayesian inferenceBayesian probabilityOperating systemConfidence intervalAdvanced Causal Inference TechniquesDecision-Making and Behavioral EconomicsAuditing, Earnings Management, Governance
Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Comment | Litcius