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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.

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File URL: http://research.economics.unsw.edu.au/RePEc/papers/2009-01.pdf
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Bibliographic Info

Paper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2009-01.

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Length: 31 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:swe:wpaper:2009-01

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Cited by:
  1. Wang, Yafeng & Graham, Brett, 2010. "Simulation Based Estimation of Discrete Sequential Move Games of Perfect Information," MPRA Paper 23153, University Library of Munich, Germany.
  2. 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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