arXiv 9 Jan 2024 · Econometrics
arXiv:2401.04512 · PDF · DOI · OpenAlex · Extracted main text
We propose a robust Bayesian method for economic models that can be rejected by some data distributions. The econometrician starts with a refutable structural assumption which can be written as the intersection of several assumptions. To avoid the assumption refutable, the econometrician first takes a stance on which assumption $j$ will be relaxed and considers a function $m_j$ that measures the deviation from the assumption $j$. She then specifies a set of prior beliefs $\Pi_s$ whose elements share the same marginal distribution $\pi_{m_j}$ which measures the likelihood of deviations from assumption $j$. Compared to the standard Bayesian method that specifies a single prior, the robust Bayesian method allows the econometrician to take a stance only on the likeliness of violation of assumption $j$ while leaving other features of the model unspecified. We show that many frequentist approaches to relax refutable assumptions are equivalent to particular choices of robust Bayesian prior sets, and thus we give a Bayesian interpretation to the frequentist methods. We use the local average treatment effect ($LATE$) in the potential outcome framework as the leading illustrating example.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Giacomini, R. and T. Kitagawa (2021) Robust bayesian inference for set-identified models | 1.000 | 9 | 3 | 100% |
| 2 | Liao, M (2024) Extending economic models with testable assumptions: Theory and applications self | 0.956 | 8 | 3 | 88% |
| 3 | Kitagawa, T (2015) A test for instrument validity | 0.928 | 5 | 3 | 80% |
| 4 | Christensen, T. and B. Connault (2023) Counterfactual sensitivity and robustness | 0.885 | 13 | 3 | 69% |
| 5 | Ghosal, S. and A. Van der Vaart (2017) Fundamentals of nonparametric Bayesian inference, Volume 44 | 0.874 | 6 | 3 | 67% |
| 6 | Imbens, G. and J. Angrist (1994) Identification and estimation of local average treatment effects | 0.811 | 4 | 2 | 100% |
| 7 | Mourifié, I. and Y. Wan (2017) Testing local average treatment effect assumptions | 0.737 | 3 | 2 | 100% |
| 8 | Card, D (1993) Using geographic variation in college proximity to estimate the return to schooling | 0.644 | 2 | 2 | 100% |
| 9 | De Chaisemartin, C (2017) Tolerating defiance? local average treatment effects without monotonicity | 0.644 | 2 | 2 | 100% |
| 10 | Dahl, C. M., M. Huber, and G. Mellace (2023) It is never too late: A new look at local average treatment effects with or without defiers | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 26 scored citations.