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Semiparametric Estimation of Signaling Games

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  • Kyoo il Kim

    (SMU)

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    Abstract

    This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identies the payos structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a renement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specified, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria.

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    Bibliographic Info

    Paper provided by East Asian Bureau of Economic Research in its series Labor Economics Working Papers with number 22452.

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    Date of creation: Jan 2006
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    Handle: RePEc:eab:laborw:22452

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    Related research

    Keywords: Semiparametric Estimation; Signaling Game; Set Inference; Sieve Simultaneous Conditional MLE;

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    1. Mark Walker & John Wooders, 2001. "Minimax Play at Wimbledon," American Economic Review, American Economic Association, vol. 91(5), pages 1521-1538, December.
    2. Elie Tamer & Federico Ciliberto, 2004. "Market Structure and Multiple Equilibria in Airline Markets," Econometric Society 2004 North American Winter Meetings 517, Econometric Society.
    3. Aradillas-Lopez, Andres, 2010. "Semiparametric estimation of a simultaneous game with incomplete information," Journal of Econometrics, Elsevier, vol. 157(2), pages 409-431, August.
    4. Martin Pesendorfer & Philipp Schmidt-Dengler, 2003. "Identification and Estimation of Dynamic Games," NBER Working Papers 9726, National Bureau of Economic Research, Inc.
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    6. Toivanen, Otto & Waterson, Michael, 2000. "Empirical research on discrete choice game theory models of entry: An illustration," European Economic Review, Elsevier, vol. 44(4-6), pages 985-992, May.
    7. Davis, Peter, 2006. "Estimation of quantity games in the presence of indivisibilities and heterogeneous firms," Journal of Econometrics, Elsevier, vol. 134(1), pages 187-214, September.
    8. Bresnahan, Timothy F. & Reiss, Peter C., 1991. "Empirical models of discrete games," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 57-81.
    9. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, 08.
    10. Arcidiacono, Peter & Jones, John B., 2000. "Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm," Working Papers 00-16, Duke University, Department of Economics.
    11. Newey, Whitney K., 1997. "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol. 79(1), pages 147-168, July.
    12. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
    13. Otsu, Taisuke, 2011. "Empirical Likelihood Estimation Of Conditional Moment Restriction Models With Unknown Functions," Econometric Theory, Cambridge University Press, vol. 27(01), pages 8-46, February.
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    Cited by:
    1. Kyoo il Kim, 2006. "Set Inference for Semiparametric Discrete Games," Working Papers 16-2006, Singapore Management University, School of Economics.

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