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New Perspectives on Statistical Decisions Under Ambiguity

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  • Jörg Stoye

    ()
    (Department of Economics, Cornell University, Ithaca, New York 14853)

Abstract

This 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 Info

Article provided by Annual Reviews in its journal Annual Review of Economics.

Volume (Year): 4 (2012)
Issue (Month): 1 (07)
Pages: 257-282

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Handle: RePEc:anr:reveco:v:4:y:2012:p:257-282

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Related research

Keywords: statistical decision theory; minimax; minimax regret; treatment choice; partial identification;

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Cited by:
  1. repec:bos:wpaper:wp2013-001 is not listed on IDEAS
  2. Tamini, Lota D., 2012. "Optimal quality choice under uncertainty on market development," MPRA Paper 40845, University Library of Munich, Germany.
  3. Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.
  4. 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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