arXiv 1 Sep 2026 · Econometrics
arXiv:2609.01943 · PDF · Extracted main text
To study a scalar parameter, a researcher may consider multiple research designs. Based on the evidence across designs, the researcher may wish to formulate a headline estimate of the parameter. I examine how to choose this headline when it is unclear which design is most appropriate for studying the parameter. I model this setting by assuming that (i) exactly one of the designs is valid for the parameter and (ii) the researcher has ambiguity about which design is valid, represented by a class of priors over the candidate designs. To account for ambiguity, I propose reporting the headline estimate that minimizes the worst-case posterior risk over the class of priors. In three applications, I show cases where accounting for ambiguity materially affects the quantitative conclusion and cases where an existing headline is already close to optimal.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
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| 8 | Berger, James O (1985) Statistical Decision Theory and Bayesian Analysis | 0.644 | 2 | 2 | 100% |
| 9 | Betrò, Bruno and Ruggeri, Fabrizio (1992) Conditional Gamma-minimax actions under convex losses | 0.644 | 2 | 2 | 100% |
| 10 | DasGupta, Anirban and Studden, William J (1989) Frequentist behavior of robust Bayes estimates of normal means | 0.644 | 2 | 2 | 100% |
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