Working Paper: NBER ID: w31511
Authors: 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.
Keywords: No keywords provided
JEL Codes: C15; C31; C55; C7
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| simulation algorithm (C69) | econometric analysis feasibility (O22) |
| simulation algorithm (C69) | likelihood of observed pure strategy combinations (C72) |
| traditional methods (C90) | likelihood evaluation complexity (C52) |
| importance sampling technique (C15) | efficient computation (C60) |
| simulation algorithm (C69) | consistent estimates of payoff parameters (C51) |
| simulation algorithm (C69) | empirical studies of peer effects and network formation (D85) |