New Perspectives on Statistical Decisions Under Ambiguity
AbstractThis review summarizes and connects recent work on the foundations and applications of statistical decision theory. Minimax models of decisions making under ambiguity are identified as a thread running through several literatures. In axiomatic decision theory, these models motivated a large literature on modeling ambiguity aversion. Some findings of this literature are reported in a way that should be directly accessible to statisticians and econometricians. In statistical decision theory, the models inform a rich theory of estimation and treatment choice, which was recently extended to account for partial identification and thereby ambiguity that does not vanish with sample size. This literature is illustrated by discussing global, finite-sample admissible, and minimax decision rules for a number of stylized decision problems with point and partial identification.
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Bibliographic InfoArticle provided by Annual Reviews in its journal Annual Review of Economics.
Volume (Year): 4 (2012)
Issue (Month): 1 (07)
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Find related papers by JEL classification:
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
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- Tamini, Lota D., 2012. "Optimal quality choice under uncertainty on market development," MPRA Paper 40845, University Library of Munich, Germany.
- Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.
- Tamini, Lota Dabio, 2012. "Optimal quality choice under uncertainty on market development," Working Papers 148589, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
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