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Econometrics Seminar - Bryan Graham (UC Berkeley)

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Location: S2.79

Title: Simulated Maximum Likelihood Estimation of Large Games using Scenarios". This is joint work with Andrin Pelican.

Abstract: This paper introduces a new simulation algorithm for evaluating the

log-likelihood and score functions associated with a class of supermodular
complete information discrete games. The algorithm allows for payoff
function estimation in games with large numbers of players and/or
many binary actions per player (e.g., games with tens of thousands
of strategic binary actions). In such cases the likelihood of the
observed pure strategy combination may be (i) very small and (ii)
a high dimensional integral with a complex integration region. Direct
numerical integration, as well as accept-reject Monte Carlo integration,
are computationally impractical in such settings. In contrast, our
method allows for accurate likelihood simulation with modest numbers
of simulation draws. Use cases include simulated maximum likelihood
(SML) parameter estimation in models of technology adoption, peer
effects, and strategic network formation.

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