arXiv 9 May 2022 · Theoretical Economics
arXiv:2205.04573 · PDF · DOI · OpenAlex · Extracted main text
When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions. That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a pre-specified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.
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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 | Epstein and Schneider (2007) Learning under Ambiguity | 0.928 | 4 | 3 | 100% |
| 2 | Cheng (2021) Relative Maximum Likelihood updating of ambiguous beliefs self | 0.644 | 2 | 2 | 100% |
| 3 | Epstein and Schneider (2008) Ambiguity, Information Quality, and Asset Pricing | 0.585 | 3 | 1 | 100% |
| 4 | Reshidi, Thereze and Zhang (2020) Information Aggregation under Ambiguity | 0.511 | 2 | 1 | 100% |
| 5 | Wang (1993) ON THE NUMBER OF SUCCESSES IN INDEPENDENT TRIALS | 0.511 | 2 | 1 | 100% |
| 6 | Brown, Cai and DasGupta (2001) Interval Estimation for a Binomial Proportion | 0.405 | 1 | 1 | 100% |
| 7 | Chen (2019) Sequential learning under informational ambiguity | 0.405 | 1 | 1 | 100% |
| 8 | De Filippis, Guarino, Jehiel and Kitagawa (2021) Non-Bayesian updating in a social learning experiment | 0.405 | 1 | 1 | 100% |
| 9 | Epstein, Kaido and Seo (2016) Robust Confidence Regions for Incomplete Models | 0.405 | 1 | 1 | 100% |
| 10 | Frick, Iijima and Ishii (2021) Welfare comparisons for biased learning | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 49 scored citations.