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Knightian Decision Theory and Econometric Inference

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    Abstract

    In this paper I attempt to reconcile the apparent definiteness of econometric practice with the vagueness of subjective probabilities assumed in Knightian decision theory. I argue that some standard uses of classical inference are Knightian in spirit, even though the formal justification of classical methods uses the frequentist notion of probability. Classical confidence regions may be viewed as defining sets of posterior means corresponding to a standardized set of prior distributions. Tests of the null hypothesis that a parameter equals a particular value may be viewed as determining whether it is rational, from a Knightian point of view, to act as if the null hypothesis were true. This interpretation of the tests seems to correspond fairly well to practice and to the informal story told by classical statisticians. Hence, one could argue that to this extent classical statisticians act unconsciously as Knightian decision makers. If one accepts this argument, then it is of interest to know what level of uncertainty aversion corresponds to the popular 5% significance level.

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    File URL: http://cowles.econ.yale.edu/P/cd/d08b/d0868.pdf
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    Bibliographic Info

    Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 868.

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    Length: 47 pages
    Date of creation: Mar 1988
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    Handle: RePEc:cwl:cwldpp:868

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    Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

    Related research

    Keywords: Decision; theory; classical statistics; probability intervals; subjective probability; Bayesian;

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    1. Truman F. Bewley, 1986. "Knightian Decision Theory: Part 1," Cowles Foundation Discussion Papers 807, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:
    1. Raman Uppal & Lorenzo Garlappi & Tan Wang, 2004. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," Money Macro and Finance (MMF) Research Group Conference 2004 54, Money Macro and Finance Research Group.
    2. Larry Epstein & Martin Schneider, 2006. "Learning Under Ambiguity," RCER Working Papers 527, University of Rochester - Center for Economic Research (RCER).
    3. Peter C.B. Phillips, 1988. "Reflections on Econometric Methodology," Cowles Foundation Discussion Papers 893, Cowles Foundation for Research in Economics, Yale University.
    4. Kin Chung Lo, 1998. "Epistemic Conditions for Agreement and Stochastic Independence of epsilon-Contaminated Beliefs," Working Papers 1998_02, York University, Department of Economics.

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