Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/321369 
Authors: 
Year of Publication: 
2025
Series/Report no.: 
I4R Discussion Paper Series No. 242
Publisher: 
Institute for Replication (I4R), s.l.
Abstract: 
For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades off a "Darwinian Learning" effect from theorizing first with a "Statistical Learning" effect from examining the data first. This short paper formalizes the tradeoff in a Bayesian model. In the modern era of mature economic theory and enormous datasets, I argue that post hoc theorizing is typically optimal.
Subjects: 
Publication Bias
Machine Learning
Predictivism vs Accommodation
HARKing
JEL: 
B41
C18
C11
Document Type: 
Working Paper

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