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On mu from the logistic quantal-response equilibrium

Author

Listed:
  • Robert Ferenc Veszteg

    (Waseda University)

Abstract

This note reflects on the key parameter of the popular logistic quantal-response equilibrium in order to set some common guidelines on its empirical interpretation. It is stressed that the estimated model must be in harmony with the experimental design, because the estimation results on mu prove to be sensitive to changes in the strategy sets of players even if those are unimportant from a game-theoretic point of view. It is also shown that a simple post-estimation correction of mu can help inter-game comparisons, while pre-estimation treatments of the data may introduce unwanted biases.

Suggested Citation

  • Robert Ferenc Veszteg, 2012. "On mu from the logistic quantal-response equilibrium," Economics Bulletin, AccessEcon, vol. 32(1), pages 102-111.
    • Handle: RePEc:ebl:ecbull:eb-11-00544
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I1-P11.pdf
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    References listed on IDEAS

    as
    1. Philip A. Haile & Ali Hortaçsu & Grigory Kosenok, 2008. "On the Empirical Content of Quantal Response Equilibrium," American Economic Review, American Economic Association, vol. 98(1), pages 180-200, March.
    2. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    3. Jacob Goeree & Charles Holt & Thomas Palfrey, 2005. "Regular Quantal Response Equilibrium," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 347-367, December.
    4. Philip A. Haile & Ali Hortaçsu & Grigory Kosenok, 2004. "On the Empirical Content of Quantal Response Models," Levine's Bibliography 122247000000000218, UCLA Department of Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    estimation; experiments; Nash equilibrium; quantal response equilibrium;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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