Optimal Post-Hoc Theorizing
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911026996314112 |
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| author | Chen, Andrew Y. |
| author_facet | Chen, Andrew Y. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Post-Hoc Theorizing Chen, Andrew Y. Econometrics General Finance Methodology 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. |
| title | Optimal Post-Hoc Theorizing |
| topic | Econometrics General Finance Methodology |
| url | https://arxiv.org/abs/2505.10370 |