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Scenario sampling for large supermodular games

Author

Listed:
  • Bryan S. Graham
  • Andrin Pelican
Abstract
This paper introduces a simulation algorithm for evaluating the log-likelihood function of a large supermodular binary-action game. Covered examples include (certain types of) peer effect, technology adoption, strategic network formation, and multi-market entry games. More generally, the algorithm facilitates simulated maximum likelihood (SML) estimation of games with large numbers of players, T, and/or many binary actions per player, M (e.g., games with tens of thousands of strategic actions, TM = O(10⁴)). In such cases the likelihood of the observed pure strategy combination is typically (i) very small and (ii) a TM-fold integral who region of integration has a complicated geometry. Direct numerical integration, as well as accept-reject Monte Carlo integration, are computationally impractical in such settings. In contrast, we introduce a novel importance sampling algorithm which allows for accurate likelihood simulation with modest numbers of simulation draws.

Suggested Citation

  • Bryan S. Graham & Andrin Pelican, 2023. "Scenario sampling for large supermodular games," CeMMAP working papers 15/23, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:15/23
    DOI: 10.47004/wp.cem.2023.1523
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    References listed on IDEAS

    as
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