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Estimating Sequential-move Games by a Recursive Conditioning Simulator

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  • Shiko Maruyama

    () (School of Economics, The University of New South Wales)

Abstract

Sequential decision-making is a noticeable feature of strategic interactions among agents. The full estimation of sequential games, however, has been challenging due to the sheer computational burden, especially when the game is large and asymmetric. In this paper, I propose an estimation method for discrete choice sequential games that is computationally feasible, easy-to-implement, and e¢ cient, by modifying the Geweke-Hajivassiliou-Keane (GHK) simulator, the most widely used probit simulator. I show that the recursive nature of the GHK simulator is easily dovetailed with the sequential structure of strategic interactions.

Suggested Citation

  • Shiko Maruyama, 2009. "Estimating Sequential-move Games by a Recursive Conditioning Simulator," Discussion Papers 2009-01, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2009-01
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2009-01.pdf
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    Cited by:

    1. Yafeng Wang & Brett Graham, 2013. "Generalized Maximum Entropy Estimation of Discrete Sequential Move Games of Perfect Information," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    2. Wang, Yafeng & Graham, Brett, 2010. "Simulation Based Estimation of Discrete Sequential Move Games of Perfect Information," MPRA Paper 23153, University Library of Munich, Germany.
    3. Nathan Yang, 2012. "Burger King and McDonald’s: Where’s the Spillover?," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 19(2), pages 255-281, July.
    4. Wang, Yafeng & Graham, Brett, 2009. "Generalized Maximum Entropy estimation of discrete sequential move games of perfect information," MPRA Paper 21331, University Library of Munich, Germany.

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